Search

As a roadmap for carbon mitigation, reducing energy consumption and improving thermal comfort are key factors for achieving efficient and sustainable buildings. The integration of Phase Change Materials (PCM) as passive thermal regulators in thermal energy storage systems is an effective measure to reduce demand of fossil fuels and enhance building thermal mass. Nevertheless, there is still a scarcity of experimental research that combines the focuses on the type and positioning of PCM in buildings components with a large-scale experimental setup, to accurately evaluate the real effects of these materials. In this framework, the present research focuses on thermal characterization and performance analysis of a Light Steel Framing (LSF) building enhanced with macroencapsulated salt hydrate based PCM. Therefore, an experimental campaign was performed in two real scale and comparable buildings. First, the thermal properties of the material and construction solutions were tested and characterized, followed by a thorough evaluation of the building performance. A comparative analysis of the inner surface temperatures in the two buildings − one representing a common LSF solution as a reference solution; and the other representing a thermally enhanced building incorporating PCM − was conducted, as well as the detailed characterization of the temperature profile in all layers of the construction solutions. Monitoring scheme was also defined to study the PCM behaviour in terms of charging and discharging process. Results revealed a significant passive thermal regulation capacity in the thermally enhanced building, reducing inner surface temperature peaks up to 5 °C to 8 °C during the winter and spring seasons. In the cooling season, PCM mobilization was hindered by the high outdoor temperatures. Considering that the PCM went through the phase transition only approximately 20 % to 28 % of the monitored period, optimizing its performance is a further issue that involves either repositioning the PCM layer within the construction assemblies or selecting a different operating temperature range. [ABSTRACT FROM AUTHOR]

The world faces critical challenges such as climate change, an increase in energy consumption, and increased CO2 emissions. The building sector plays a significant role in exacerbating these issues. Designing sustainable buildings has grown increasingly complex, necessitating the optimization of building shading skins to enhance overall performance. Integrating inspiration from nature into design can significantly improve sustainability. Nevertheless, current frameworks for developing biomimetic adaptive building shading skins encounter limitations in efficiently translating design concepts from nature into practical applications. This study proposes a biomimetic shading skin design inspired by the Saguaro Cactus, reviewing existing literature, introducing a novel simulation framework, and exemplifying its implementation through a parametric case study. A comprehensive evaluation system assesses 1,384 simulations, utilizing Octopus and Grasshopper plug-ins for Rhino to improve energy consumption in an office building. Evaluation criteria include Energy Use Intensity (EUI), Thermal Loads, and Spatial Daylight Autonomy (sDA). The results show that the multi-angle biomimetic shading skin reduced energy consumption by 20 %, with a 36.5 % reduction in cooling load relative to the baseline model. The daylighting assessment demonstrates that up to 97 % of interior spaces receive adequate daylighting. The results underscore the superiority of biomimetic shading skin in enhancing both cooling load and energy consumption, as well as daylight performance and visual comfort. The result shows that the framework's flexibility offers an extensive range of opportunities, appealing to designers and researchers who are motivated to harness its capabilities to enhance diverse design factors. [ABSTRACT FROM AUTHOR]

This comprehensive study analyses the energy demand for heating and cooling in the building stock across different climatic zones in Spain, incorporating the effects of weather evolution over the past 30 years (from 1992 to 2022). In evaluating 7200 cases across four climatic years and six climatic zones which include six Spanish cities, the analysis examines the construction characteristics of the building stock. The focus is on the thermal envelop properties under two scenarios: the current state and a renovated state that complies with the new Spanish building code (in agreement with the EPBD EU-Directive EU/2024/1275). The study's findings reveal potentially significant changes in energy demand due to climate. The heating demand decreases significantly due to increasing temperatures ranging from 17% in Madrid up to 37% in Almería. This effect is contrasted by a substantial increase in cooling demand, which has surged 223% in Barcelona, a surge closely linked to higher summer temperatures. The paper concludes that the current energy certification scheme fails to provide a real overview of the energy performance and needs of buildings in Spain. In particular, it underestimates actual sensible cooling demand by 56% in Barcelona and 41% in Almería. Additionally, the failure of existing building regulations to address the issue of indoor overheating is proven. [ABSTRACT FROM AUTHOR]

[Display omitted] • Hybrid cladding, optimized by NSGA-2 and Wallacei, meets LEED standards. • Aluminum cladding fails to meet LEED due to insufficient sDA. • Polycarbonate cladding meets sDA but fails LEED on ASE criteria. • The optimized model comprises 93.18% aluminum and 6.82% polycarbonate. • Skylight distribution is as follows: North 0%, East 13.59%, South 7.72%, West 10.96%. Given the significant impact of the construction sector on climate change, it is crucial to develop strategies and integrate diverse knowledge to enable architects to make informed decisions in the early stages of design. Transitioning to positive buildings requires a multidisciplinary approach, integrating various scientific disciplines in architectural design. This research evaluates cladding systems as passive design strategies to reduce environmental impacts and achieve LEED certification during the operational phase of buildings. Focusing on a large office building in Boston, the study assesses three types of cladding—aluminum, polycarbonate, and a hybrid system—examining their roles in energy consumption, Annual Sunlight Exposure (ASE<10 %), and Spatial Daylight Autonomy (sDA>50 %). The research offers a comprehensive analysis using advanced parametric tools like Rhino Grasshopper for modeling, Honeybee and Ladybug for climatic analysis, OpenLCA for environmental impact assessments, Wallacei for optimization, and Design Explorer for model selection. Initial findings indicate that aluminum cladding is thermally favorable but does not meet LEED sDA standards. Polycarbonate cladding meets sDA levels but fails ASE criteria. To address these deficiencies, a hybrid system was optimized using the NSGA-2 algorithm and Wallacei selection methods, meeting both sDA and ASE criteria and demonstrating enhanced sustainability. This system, comprising approximately 93.18 % aluminum and 6.82 % polycarbonate, optimizes daylight autonomy and minimizes thermal gains and losses, with strategic skylight distribution—13.58 % east, 7.72 % south, and 10.96 % west. The strategic use of advanced parametric and optimization software in this research empowers evidence-based decision-making in sustainable architecture, emphasizing the need for a holistic approach to achieve optimal outcomes. [ABSTRACT FROM AUTHOR]

• An evaluation system on the building's thermal environment is proposed. • The method of constructing the model with open interfaces is explored for traditional/smart buildings. • Evaluation models for different functions, heating periods, and communities are established. • The correction factors are proposed for age, gender, BMI, and metabolic rate. A healthy and comfortable thermal environment has become the core focus on building environment evaluation. The key is to realize room temperature regulation based on the demand for human thermal comfort. The study aims to build the core of building thermal environment regulation, building thermal environment evaluation system, to achieve accurate evaluation and prediction of thermal comfort temperature for different type buildings and occupants. Considering the differences between traditional and smart buildings in building intelligence, whose thermal environment evaluation frameworks are proposed respectively. Taking the traditional buildings in a typical city from the severe cold zone as an example, the thermal comfort models of different type buildings, heating periods, and occupants are established. Furthermore, the corresponding correction factors are proposed for age, gender, BMI and metabolic rate. This study provides theoretical support for the evaluation of building thermal environments. [ABSTRACT FROM AUTHOR]

[Display omitted] • Surrogate-assisted sustainable building design optimization studies are discussed. • Global disparities in surrogate modeling and optimization practices are revealed. • Efficient surrogates foster high-dimensional passive design optimization approaches. • Recent advances in surrogate modeling facilitate context-diversified optimizations. • Contextual variations prompt versatile pre-experimentation for suitable techniques. Artificial Intelligence (AI) applications in building performance prediction for environmental sustainability outcomes play a significant role in compensating for computationally incompetent and expensive approaches to solving increasingly complex optimization problems. Although machine-learning-based surrogate models (SMs), one of the many AI approximation strategies for higher-order models, have long been utilized in sophisticated optimization studies in various fields, their reliability in the field of sustainable and low-energy architectural design is now being extensively explored. To comprehend the effectiveness of applying state-of-the-art surrogate-assisted optimization methodologies for building energy performance, this study presents a comprehensive review of recent studies aimed at identifying innovative theories and critical factors for each component of the optimization process. Considering passive form and envelope design variables, seventy-two relevant studies from the Scopus database were selected for review after screening. The results, including the current trends for surrogate modeling and optimization methods, accuracy levels of SMs, extent of building performance improvement, and decisive building design variables, are thoroughly discussed according to the varying conditions of each study. Finally, a further discussion of recent methodological advancements and limitations can help identify potential approaches and challenges for future research. [ABSTRACT FROM AUTHOR]

Customers in the government, institutions (GI) and office buildings (OB) sectors face substantial financial losses when their operations are interrupted by power distribution grid failures. Several factors affect the amount of interrupted energy, also known as Expected Energy Not Supplied (EENS) as well as the Expected Customer Outage Cost (ECOST). These factors include line/cable failure rates, restoration time, customer peak load, load and cost models, and the time of failure occurrence. Estimation results may also vary based on whether the customer distribution feeder is an overhead line or an underground cable. Therefore, this article focuses on a sensitivity analysis to determine the impact of above-mentioned factors on the EENS and ECOST for GI and OB customer sectors resulting from stochastic failures in power distribution networks. The GI and OB customers connected to the benchmark networks provided by Roy Billinton are taken into consideration. Sensitivities are estimated using an efficient and robust stochastic multilevel Monte Carlo simulation technique. A comparison of estimated outcomes for overhead and underground distribution systems is carried out. The findings show that ignoring variability in factors can lead to inaccurate planning and operational decisions related to EENS and ECOST for GI and OB customers. [ABSTRACT FROM AUTHOR]

• Cool roofs can provide a 25 % reduction of cooling energy demand of office stock. • Cool roofs can provide a 6 % reduction of the current heating and cooling energy demand. • Cool roofs would reduce up to 12 % of future heating and cooling annual energy demand. • Cool roofs will help to reduce future offices' energy demand in all climate zones. • Improvement in the thermal comfort ranges from 26 % to 78 % of discomfort degree days. In a context of climate change, the cooling period of buildings is increasing every year in Mediterranean countries. This will become a relevant issue when existing buildings are retrofitted. Cool roofs have a passive cooling effect on a building and can contribute to decreasing the annual cooling demand. In office buildings, thermal comfort has a considerable influence on workers' performance. In these buildings cooling energy demands tend to be high due to the high internal thermal loads. This paper describes annual energy simulations of Spanish office stock to evaluate the potential energy savings of cool roofs within the context of building retrofitting. The energy demand and thermal comfort of representative office buildings is compared before and after the application of a cool roof coating, in the present time and in the year 2050, for all climate areas of Spain, under the most optimistic and most pessimistic future weather scenarios. Results showed that the cooling energy demand of Spanish office stock can be potentially reduced by 25 % when a cool roof is applied, and the annual energy demand for heating and cooling can be cut by 6 %, 11 % and 12 % in the present, the optimistic future scenario and the pessimistic future scenario, respectively. [ABSTRACT FROM AUTHOR]

• Predicting building occupancy status using integrative ANNs. • Testing novel metaheuristic algorithms to enrich the literature. • Highest efficiency of SCE due to higher accuracy and faster optimization. • From PCA results, CO2, Humidity, and time are critical predictors. The importance of office occupancy detection is evident, due to its critical role in workplace management and facility optimization. Recent studies have recommended the use of data-mining techniques for this purpose. In this research, a powerful hybrid model is developed upon artificial neural network (ANN) coupled with shuffled complex evolution (SCE) algorithm for the binary prediction of occupancy in an office room based on several characteristics such as light, temperature, humidity, and CO 2. The trained model is tested via two datasets (considering the opened/closed office door while occupied) and according to the results, it achieved an excellent prediction. In detail, the values of area under the curve (AUC) were above 0.98 along with mean square errors below 0.03; indicating the superiority of the proposed SCE algorithm compared to three baseline techniques, namely Archimedes optimization algorithm (AOA), incomprehensible but Intelligible-in-time logics (ILA), and gazelle optimization algorithm (GOA) in this study, as well as some others in earlier literature. Moreover, the SCE-ANN emerged as the most efficient (i.e., the quickest and least complex) algorithm. In the end, the dataset is exposed to a well-known statistical method called principal component analysis (PCA) to evaluate the importance of the considered input factors and identify the most crucial ones. Altogether, the findings of this study can provide valuable environmental and economic contributions to related sectors such as building energy management. [ABSTRACT FROM AUTHOR]

[Display omitted] • Social Network Analysis and Text Mining to analyze 8024 abstracts on LCA and Building. • The semantic brand score measures the semantic importance of seven issues related to Building and Sustainability. • Limited publications assessing the social impact of LCA in buildings. • Materials are the most central issue in the literature on sustainable buildings. Research on Life Cycle Assessment (LCA) is being conducted in various sectors, from analyzing building materials and components to comprehensive evaluations of entire structures. However, reviews of the existing literature have been unable to provide a comprehensive overview of research in this field, leaving scholars without a definitive guideline for future investigations. This paper aims to fill this gap, mapping more than twenty years of research. Using an innovative methodology that combines social network analysis and text mining, the paper examined 8024 scientific abstracts. The authors identified seven key thematic groups, building and sustainability clusters (BSCs). To assess their significance in the broader discourse on building and sustainability, the semantic brand score (SBS) indicator was applied. Additionally, t building and sustainability trends were tracked, focusing on the LCA concept. The major research topics mainly relate to building materials and energy efficiency. In addition to presenting an innovative approach to reviewing extensive literature domains, the article also provides insights into emerging and underdeveloped themes, outlining crucial future research directions. [ABSTRACT FROM AUTHOR]

• Habitability and life conditions diagnosis based on real monitoring. • Quantifies the overheating risk in naturally conditioned buildings. • Proposes an innovative passive cooling-based roof solution for heat dissipation. • The new roof solution helps occupants endure heat waves without indoor discomfort. Renovating buildings is a crucial mission for the coming decades to combat energy inefficiencies and produce a resilient building stock. The inefficient thermal envelope has significant implications for occupants, especially for families lacking the financial resources to maintain a healthy and comfortable indoor environment. In scenarios where households cannot afford or utilize HVAC systems and high temperatures are more frequent, building retrofits should prioritize reducing heat loss in winter and passively lowering indoor temperatures in the cooling season. This work introduces a new solution to enhance the liveability of a social housing building in southern Spain by integrating a double-skin roof with a conventional retrofit strategy, which incorporates passive cooling techniques – night ventilation and an evaporative cooling system – for effective heat dissipation based on available commercial solutions. An extensive monitoring campaign was conducted over three years to evaluate the thermal comfort of occupants and the risk of overheating in naturally conditioned buildings before and after renovation following two impact assessment methods: real-time data-based and simulation-based. The real-time data-based method compared the indoor conditions of a dwelling with a conventionally retrofitted roof to one with a double-skin roof, indicating that the dynamic roof maintains a ceiling temperature lower than the air temperature throughout the summer while the conventional roof acts as a heating surface. On the other side, the simulation-based method compared the observed indoor conditions with a double-skin roof enabled during 2023 to the initial stage and a double-skin roof disabled scenario, using calibrated building energy models. The natural cooling roof solution almost eliminates the overheating events, leading to a reduction of 94.1% and 76.9% in discomfort degree-hours compared to the initial stage according to the ASHRAE adaptative and Fanger comfort models, respectively. Additionally, the results indicate that conventional retrofits can increase the risk of overheating when a cooling system is not considered. Integrating a cool roof solution requires only an extra implementation cost of 31€/m2 compared to a conventional solution, having an operational cost that represents less than 3% of the minimum monthly income in the social housing district. [ABSTRACT FROM AUTHOR]

• The present work performed a detailed analysis of trapezoid profile shadings to provide alternative to plane/flat shaped shadings for horizontal, vertical, overhang and egg crate louvres types. • The effect of slat tilt angle using conventional and split louvres was examined to increase UDI and DA values. • For south orientation, the shading using rectangular ribs (profile 3) can achieve UDI 500-1000 lx metrics of 97.22 % and target uniformity level of 0.60. • Shipping container homes can provide a practical alternative to tackle the expensive house problems. The present work explores the impact of shading devices on prefabricated buildings. A large office conference room constructed by reusing two 40-foot high-cube ISO (global) standard containers is considered for an integrated thermal-visual effect in various building orientations. In this study, the trapezoid profile louver shadings, designed from leftover corrugated side walls of the shipping container, are used to protect the fully glazed façade. Parametric modeling was performed using EnergyPlus and RADIANCE, running on the platform of Grasshopper plugin for Rhinoceros 3D. The trapezoidal profile louver shadings were evaluated under different orientations and shading configurations. At lower slats length of 50 mm, trapezoid horizontal shadings resulted in energy savings of 40.78 %, 44.89 %, 42.40 %, 43.59 %, and 40.03 % for the east, southeast, south, southwest, and west-oriented facades, respectively. In comparison, plane/flat-shaped shadings resulted in savings of 27.77 %, 31.52 %, 34.27 %, 31.49 %, and 27.50 %. The fixed exterior split louvers configurations (α45° β30° γ45°) improved the annual Useful daylight illuminance (UDI) and Daylight Autonomy (DA) values to 92.60 % and 67.23 % for trapezoid shades, while the conventional tilt angle (α45° β45° γ45°) achieved 87.82 % and 51.14 %. Furthermore, the energy savings potential was nearly the same values of 48.59 % and 47.55 %. Considering the 21st of June at 12:00p.m. for the south facade, the UDI 500-1000 lx metric of split louvers improved to 88.89 %, while the conventional tilt angle achieved 75.93 %. The study investigating different shading profiles can achieve UDI 500-1000 lx metrics of 97.22 % and maintain the uniformity level of 0.60 by using rectangular ribs (profile 3). The findings underscore the significant potential of innovative trapezoid profile louver shading devices in enhancing energy efficiency and daylight performance in prefabricated buildings. [ABSTRACT FROM AUTHOR]

Developing Urban Building Energy Models (UBEM) to manage heat decarbonisation at the neighbourhood level is both crucial and challenging. Our study addresses key research challenges: quantifying peak heat energy demand and developing reliable and replicable urban energy demand microsimulations. We applied our UBEM approach to a case study area of 228 houses served by the Ridgeway New Low Voltage (LV) electrical substation in Newcastle upon Tyne, to estimate the difference between existing peak heat and electricity demand at this scale. Results show that peak heat demand is 5 to 14 times greater than peak electricity demand, significantly exceeding estimates from national studies. Furthermore, our work follows a comprehensive validation framework, comparing our simulation results against all relevant and available UK datasets. This comparison demonstrates that our model is an overall good fit at the declared Level of Detail. However, our paper identifies significant challenges, particularly in validation, that need to be addressed to improve the reliability and usability of future UBEMs. Finally, we reflect on all our findings and make policy recommendations, as we believe addressing the issues raised in this paper is vital for enabling area-based planning of residential heating electrification, particularly in the UK. [ABSTRACT FROM AUTHOR]

The Energy Performance of Buildings Directive introduced the Smart Readiness Indicator (SRI), a common European system for assessing buildings' smart readiness. While not mandatory across the European Union, some countries have initiated trial phases, and several studies already examined its application in different Member States. In this work, we present the findings of an extensive application of the SRI methodology on a sample of 59 recently constructed or recently renovated high-performance buildings in South Tyrol, Italy. Our research focused on two main objectives: (1) characterizing the level of smart readiness of state-of-the-art modern buildings in the Italian Alpine region of South Tyrol, and (2) discussing the potential correlation between the level of smart readiness and building energy performance class. Specifically, the HVAC and building systems with the most widespread adoption of advanced smart technologies were identified, as well as those configurations with traditional control solutions suggesting potential for improvement. The analysis proved that the different building categories present various levels of smart readiness, which is higher for the local office buildings and lower for the residential ones. No particular correlation was found between smart readiness and energy performance class, suggesting the need of adopting the SRI to give a more comprehensive characterization of the building performances. [ABSTRACT FROM AUTHOR]

• Existing allocation models for jointly-owned assets in MOBs do not cater to energy. • Energy allocation models are building-typology and objective dependent. • Evidence-based decision-making aids the stakeholders in adopting RES. • Tailored policy approaches and targeted incentives may boost RES adoption. Adopting Renewable Energy Systems (RES) in Multi-Owned Buildings (MOBs) is critical for achieving sustainability goals, yet the equitable allocation of energy from a jointly-owned RES to individual apartments remains overlooked in practice and literature. Current practices, rooted in models of common cost allocation, fail to address the dynamic traits of energy allocation and disregard the energy entitlement of each apartment, necessitating a tailored approach for renewable energy allocation in MOBs. This paper emphasises energy entitlement and introduces a novel, evidence-based decision-making framework assessing nine distinct energy allocation models for their suitability in diverse MOB typologies, characterised by physical and social factors, and presents a ranked list. Our findings reveal extensive variation in model suitability depending on the building typology. Equal energy allocation minimised financial disparities, while demand-based allocation was significantly effective for older, mid-rise buildings with fewer tenants. Conversely, the flat-fee model was found unsuitable regardless of building type. Furthermore, the study demonstrates that the suitable model for a building typology may not always align with the objectives of RES installation, thus endorsing an 'objective proximity' analysis. The proposed framework serves as a valuable guide for stakeholders, including the owners' corporations, policymakers, and industries, to make well-informed decisions for a smooth transition to renewable energy. It lays the foundation for potential expansion to other building types while underscoring the necessity for adaptive policies in promoting RES adoption. [ABSTRACT FROM AUTHOR]

Exploiting the energy flexibility resulting from thermal loads management in buildings is one of the most promising solutions to contribute to the energy transition targets. To offer energy flexibility to the grid, the building should meet the requirements: (i) it needs electrically powered generation (e.g., through Heat Pumps) and (ii) advanced control techniques (e.g., Model Predictive Controls) must be implemented. In recent years, the scientific community has produced many studies demonstrating the potential of Model Predictive Controls combined with Heat Pumps to exploit energy flexibility in buildings. However, a large-scale deployment of such control techniques is still far off, both because of the not yet widespread use of Heat Pumps and the computational challenges involved in implementing them. The aim of this study is to contribute to the deployment of advanced control techniques for Heat Pumps systems in buildings by simplifying their implementation. At this aim, validated archetypes of Model Predictive Controls for Heat Pumps in residential buildings are proposed. The availability of archetypes can greatly facilitate the practical application of Model Predictive Control. In fact, they are adaptable to the characteristics of different buildings and Heat Pump installations and their structure is designed to have an acceptable trade-off between calculation time and prediction reliability. The archetypes cover three different types of heating systems: low temperature radiators without (i) and with (ii) integrated heat storage devices and (iii) underfloor heating system. All archetypes are applied to a real Heat Pump and validated through experimental campaigns. During the experimental test all the archetypes proved to be effective in controlling the real system and the results showed good reliability of the prediction model in the control (for all archetypes a Root Mean Square Error lower than 0.44 °C was obtained) and an optimizer success rate in Model Predictive Controls greater than 91 %. The archetypes proposed are provided as open-source tools that can be reused for similar cases to facilitate Model Predictive Controls implementation in heat pump systems. [ABSTRACT FROM AUTHOR]

While green building standards are widely promoted for their energy-saving potential, limited research has explored their actual impact on electricity consumption in residential buildings. This study addresses this gap, comparing certified green buildings to standard ones using a multifaceted approach that combines high-frequency smart meter data with comprehensive socio-demographic data and detailed descriptions of building properties. Metrics investigated included total household consumption, energy use intensity (EUI), and per capita consumption, in each case differentiating between overall appliance use and dedicated heating, ventilation and air conditioning (HVAC) consumption. While green buildings achieved a 16% reduction in EUI for HVAC compared to standard buildings and thus lowered peak electricity demand, total annual household electricity consumption remained similar. The variance in electricity consumption among households in the sample indicates that occupant behaviour is a significant factor. Counter-intuitively, the research found that occupants of green buildings did not exhibit greater environmental awareness or report more energy-efficient appliance use habits compared to those in standard buildings. It also highlights limitations of EUI as the primary metric for 'efficiency', rather than total consumption. Furthermore, because consumption is normalized by floor area, this metric may in fact inadvertently incentivize larger apartments that might require even more energy overall. These findings demonstrate the need for a modified area-normalized EUI target incorporated in the building codes as well as a more comprehensive approach to develop complementary policy measures. [ABSTRACT FROM AUTHOR]

• A flexible HIL framework to emulate faults and attacks in building energy systems. • Energy performance, controller dynamics and network traffic are real-time monitored. • A joint classification framework to differentiate physical faults and cyber-attacks. • Real-time HIL experiments conducted to validate the threat detection algorithms. The increasing use of remote or mobile access, integrated wearable technologies, data exchange, and cloud-based data analytics in modern smart buildings is steering the building industry towards open communication technologies. The increased connectivity and accessibility could lead to more cyber-attacks in smart buildings. On the other hand, physical faults (e.g., HVAC − heating, ventilation, and air-conditioning faults) may have similar adverse impacts as those from the cyber-attacks on building energy systems, such as occupant discomfort, energy wastage, and equipment downtime. However, current physical behavior-based anomaly detection methods fail to differentiate between cyber-attacks and physical faults in building energy systems. Moreover, the challenge in collecting real-world threat data with ground truth has led researchers to rely on numerical models with user-defined assumptions, which may not accurately reflect real-world conditions due to the lack of in-situ experimental datasets. To address these challenges and gaps, this paper presents a flexible hardware-in-the-loop (HIL) testbed for generating cyber-attack and physical fault datasets and demonstrating threat detection algorithms in a real building automation system (BAS) environment. This testbed combines hardware (i.e., real BAS with local HVAC controllers and a physical network) with software (i.e., high-fidelity models to represent behaviors of building envelope and HVAC energy systems), enabling emulations of realistic threats. Five HIL experiments, including one baseline without any threats, two with physical faults, and two with cyber-attacks, were conducted to generate datasets containing detailed network traffic and system states. A joint classification framework, incorporating a network analyzer and a physical HVAC fault detector, was proposed to automatically detect cyber-physical abnormalities on BAS at both the network and the physical HVAC levels. The network analyzer comprises a conditional random fields (CRF) based command validator and a statistics-based detection strategy. The fault detector employs a weather and schedule-based pattern matching and feature-based principal component analysis (WPM-FPCA) method. Evaluation of the classification using four metrics from the multi-class confusion matrix revealed an average accuracy of 90.2 %, recall of 89.7 %, precision of 88.5 % and F1-score of 89.2 %. These results demonstrate that the proposed joint classification framework can effectively differentiate between specific types of cyber-attacks (e.g., device reinitialization attack, network Denial-of-Service attack) and physical faults (e.g., air handling unit operational fault, cooling coil valve stuck) in real time for improved building energy management. [ABSTRACT FROM AUTHOR]

Central air conditionings (CACs) deployed in large-scale commercial buildings have consumed a large amount of energy in cities. Growing attention has been expressed in their operating efficiency improvement and demand response (DR) participation to facilitate energy conservation and supply–demand balance. To tackle these challenges, a chance constrained economic dispatch approach is developed for CACs, aimed at optimizing their operation scheduling while accounting for DR participation. Firstly, the economic dispatch framework is established to harness capacity reserves, thermal inertias, and chance constraints to deal with cooling load uncertainties, accompanied by a three-stage DR strategy aimed at augmenting response potential. On this basis, the operation scheduling of CACs is formulated as an optimization problem to minimize overall operating costs considering DR incomes. To solve the problem, the energy-temperature transformation model is developed to quantify available thermal inertias and DR capacities within user comforts. Additionally, chance constraints are converted to specific reserve requirements, to prevent inappropriate dispatches caused by severe but rare forecast errors. Furthermore, the feasible regulation region is designed to coordinate capacity reserves and thermal inertias in response to cooling load forecast errors. Lastly, case studies are conducted using realistic data from commercial buildings, validating the effectiveness of dispatch on operating efficiency improvement, forecast uncertainty accommodation, as well as contributions to DR provision. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive simulation suite for a residential heating and cooling system that combines a vapor-compression system (VCS) with a water-based thermal energy storage (TES) using a three-fluid heat exchanger (TriCoil™). The VCS configuration includes the TriCoil™ as the indoor coil, a fin-and-tube heat exchanger as the outdoor coil, a variable-speed compressor, and an isenthalpic expansion device. To minimize computational cost for annual simulations, an artificial neural network (ANN) was employed to develop a data-driven model for the TriCoil™. The suite integrates models of the VCS, TES, and control systems, factoring in utility rates, building load profiles, and weather data. Validation against experimental data demonstrated accurate predictions, with TES temperature profiles having a root mean square error of 0.3 K and VCS heating capacity predictions showing mean absolute errors of 4.3% for the R410A side, 5.0% for the water side, 4.9% for the air side, and 4.3% for the air-sensible side. Simulations for a cooling season in Stillwater, OK, demonstrated significant electricity cost reductions by shifting cooling loads from peak to off-peak hours. The proposed system minimizes expenses using time-of-use rates and optimized load scheduling, showcasing substantial benefits even with a small TES unit. [ABSTRACT FROM AUTHOR]

• The energy consumption data of four types of public buildings in the four quarters was trained and predicted based on five machine learning models. • The energy consumption data was analyzed for influencing factor correlation based on tree model and Spearman's method. • Actual building cases were given energy-saving measures recommendations based on predictions and correlation analysis. Energy consumption prediction can help the government to formulate building energy consumption quotas and buildings energy consumption correlation analysis can provide a clear direction for buildings energy conservation and renovation. In the study, the energy consumption data of four types of public buildings in Guangxi in the four quarters of 2021 based on five machine learning models was trained and predicted. The energy consumption data was analyzed for correlation based on two methods of influencing factor correlation analysis. Finally, based on the prediction model and correlation analysis results, suggestions for energy conservation and renovation were given to eight buildings. The results showed that compared to other models, CNN had higher prediction accuracy, and the fitting regression index was generally above 0.95. The season had little impact on the characteristics correlation, but the building type had a significant impact. The electricity consumption had a high correlation with the energy consumption of all types of buildings. Through case analysis, it was concluded that some buildings did not need energy-saving renovation for the time being, while others require multiple phases of energy reduction over several quarters to meet the requirements. [ABSTRACT FROM AUTHOR]

A balanced approach that integrates renewables, energy efficiency, and energy sufficiency is essential for achieving climate neutrality and sustainability. Residential buildings are particularly significant in this context, as occupants' lifestyles shape energy demand. The review starts with an overview of energy sufficiency in building science. It then narrows its focus to investigate how energy ratings enhance energy sufficiency in residential buildings. This is done by addressing three key questions: what examples of energy sufficiency actions are referenced in the literature, and how can they be categorized; how building energy codes act as drivers of energy sufficiency; and whether energy ratings encourage the selection of more appropriately sized appliances. These questions are explored through various discussion topics, including a comprehensive list of energy sufficiency actions, the behavioral factors influencing energy excessiveness or insufficiency, and the impact of dwelling and household size on energy use. The conclusions are organized into ten key findings that underscore essential aspects of advancing energy sufficiency through future research and energy policy, emphasizing the need to rethink building codes and appliances energy rating. These findings include reinforcing the role of energy sufficiency alongside the energy-efficiency first principle, tailoring energy services to user needs while exploring alternatives to minimize unwanted use, expanding action examples beyond common practices and quantifying their impacts to strengthen implementation, using operational ratings and the gap ratio to identify areas for improvement and behaviors contributing to excessiveness, addressing unregulated energy end-uses, establishing per capita metrics for comprehensive evaluation, ensuring progressive efficiency in building codes and appliance energy ratings to prevent increases in demand due to excessive size, and developing benchmarking to address regional disparities and resource constraints. • Review focuses on how energy ratings enhance energy sufficiency in dwellings. • Explores energy sufficiency actions and their categorization. • Emphasizes rethinking building codes and appliance ratings for sufficiency. • Operational ratings and per capita assessment are crucial transitions. • Progressive energy efficiency to be included in energy ratings. [ABSTRACT FROM AUTHOR]

• Model Predictive Control (MPC) technique implemented in C++ using OR-Tools engine for the energy consumption optimization of residential buildings. • RC grey-box model developed for a 20-dwellings building which incorporates an external radiant heating wall. • Testing the MPC operation in 64 scenarios including weather conditions, occupancy behaviour and optimization criteria. • Optimization criteria comparison: energy, cost, emissions reduction; self-consumption increase. This study presents an optimization algorithm for Model Predictive Control (MPC) of the HVAC systems in multi-family residential buildings assessing the performance of four objective functions. Implemented in C++, using the free OR-Tools optimization library, the model is formulated a Mixed Integer-Linear Programming (MILP) problem. The study analyses the results of tests conducted on a 20-dwelling block in Switzerland across various weather and occupancy conditions, resulting in a parametric study of 64 cases. The models developed for the MPC are Grey-box type for the interconnected energy systems: the building, thermal storage tanks, a heat pump, the ventilation system and PV collectors, highlighting a radiant wall heating system integrated into the building facade. The tanks and the heat pump models were informed with manufacturer data, while for the building a R3C3 thermal-electrical equivalent model was developed, calibrated using TRNSYS simulations with a root mean square error of 1.7%. Findings demonstrate how the algorithm optimizes the operation according to the desired criteria, while ensuring indoor comfort with a 15-minute time resolution. The time execution of the majority of cases is under 3 min in a low-specs computer, affirming its practical viability for real-world implementation. [ABSTRACT FROM AUTHOR]

• The optimal energy storage capacity can be obtained through PSO algorithm. • The different stakeholders leads to the emergence of Pareto optimal solution sets. • P2P trading can reduce investment in shared energy storage capacity. • Combining strategies can enhance the on-site consumption rate of photovoltaic power. • P2P trading significantly increases users' electricity revenue. Constructing distributed photovoltaic systems on industrial building rooftops and establishing adaptive microgrid energy flow scheduling models are effective means of achieving full deployment and consumption of photovoltaics. The operational modes and stakeholders involved in shared energy storage and peer-to-peer trading differ significantly, influencing both the energy flow scheduling and on-site consumption rates of microgrids. In this study, a dual-objective function model with multiple constraints was designed, and particle swarm optimization was applied to seek optimal energy storage capacity. Pareto optimal solution sets under dual objectives with conflicting interests were explored, and comparative analyses of energy flow scheduling under shared energy storage and peer-to-peer trading microgrid modes were conducted. Furthermore, the daily cost-effectiveness of these two modes was evaluated. The results indicated that stakeholders with conflicting interests difficult to achieve win–win outcomes in energy scheduling. The integration of peer-to-peer trading not only reduced shared energy storage capacity by 18% but also achieved local consumption rates of 62% and 100% in summer and winter, respectively. Peer-to-peer trading had a minimal impact on user power costs, yet it increases power revenues by 32% and 235% in summer and winter, respectively, thereby reducing the net expenditure of the users. The results of this study provide a reference for microgrid energy flow scheduling models involving entities of uncommon interest in industrial parks. [ABSTRACT FROM AUTHOR]

The development of a new hybrid hydrogen generator, using both photoelectrochemical process and conventional electrolysis, integrated into a combination system for buildings is a major step forward in the quest for sustainable energy solutions in cities where it is specifically designed, implemented and evaluated for incorporation into sustainable community systems. A significant advantage of this hydrogen generator is the ability to produce hydrogen constantly, regardless of the absence of solar irradiation. Furthermore, both electrolyzers contribute to a rise in the amount of hydrogen produced. Furthermore, the utilization of PEC electrolysis for hydrogen generation results in a significant reduction in the overall power requirements of the system. This new study investigates an integrated power system that produces electricity from solar energy and tackles issues related to freshwater supply, hydrogen production, and heating/cooling demands. The purpose of this research is to integrate advanced system components, including a solar power tower system, a combined Brayton-Rankine cycle, a multi-effect desalination (MED) unit, a new hybridized hydrogen generator system, an absorption cooling cycle (ACS), and a hydrogen storage and refuelling station. The overall energy efficiency is determined to be 49%, while the exergy efficiency becomes 46.2%. [ABSTRACT FROM AUTHOR]

• PEMFC-renewable source heat pump (RSHP) is proposed for plus energy buildings. • Steady and transient simulations on performance of PEMFC-RSHPs are conducted. • Ground-source heat pump (GSHP) shows the highest energy efficiency of 0.252. • PEMFC-GSHP with gray hydrogen decreases CO 2 emissions by 70.9% over GSHP. • PEMFC-GSHP is proposed as the most feasible system for plus energy buildings. The adoption of plus energy buildings (PEBs) has been considered extensively in various nations for carbon neutralization. However, studies on suitable active systems that can efficiently supply heating, cooling, and electrical energy to PEBs are lacking. This study proposes PEMFC-assisted renewable energy source heat pumps (PEMFC-RSHPs) as active systems for plus energy buildings. In the steady-state simulations using TRNSYS, the waste-heat recovery effect was investigated at various current densities and PEMFC temperatures. In the transient simulations, the seasonal operating characteristics, energy, economic, and environmental performances of PEMFC-RSHPs using air, raw-water, and ground energy sources were analyzed. During the heating season, the maximum primary energy efficiency (PEF) of a PEMFC-assisted ground source heat pump (GSHP) was approximately 0.252 at an optimal current density of 0.8 A cm−2. However, during the cooling season, the PEMFC-assisted raw-water source heat pump (RWSHP) showed the highest PEF of 0.407 at an optimal current density of 0.7 A cm−2. In addition, the CO 2 emissions of the PEMFC-GSHP using green hydrogen were 54.3% lower than those using gray hydrogen, although the economic feasibility was still challenging. In conclusion, the PEMFC-GSHP using green hydrogen is the most feasible active system for PEBs. [ABSTRACT FROM AUTHOR]

Buildings play a significant role in global carbon emissions, and offer substantial potential for energy savings and emission reduction. This research delves into the Emission Factor Discrepancy (EFD)—the variance in CO 2 emission reduction projections obtained by employing either annual or hourly average Emission Factor (EF) for electricity generation. Through two detailed case studies in the Netherlands and incorporating emission data from the Netherlands, Sweden, and France, the study uncovers the potential magnitude and country-specific variability of the EFD. By demonstrating how the energy mix of a country influences the EFD, the research offers valuable insights into the accuracy of emission calculations for different circumstances, particularly in the context of transitioning to renewable energy sources. We have found that countries with energy sources having low load-following capability and low EFs exhibit a large EFD. Whereas, countries with high EFs and large deployment of Photovoltaics (PV) show a notably large EFD on emission reduction related to PV production. This highlights the importance of carefully selecting EFs when evaluating building retrofits in the context of smart city initiatives. This research highlights the need for establishing a uniform framework for calculating carbon emissions associated with retrofitting in buildings in conjunction with the granularity of data and the specific energy mix of a country. • Common practice of using annual emission factors in smart city projects is questioned. • This work underscores criticality of data granularity in smart city projects. • The energy mix of a country affects requirements for data granularity. • Calculations of energy related CO 2 emissions in buildings vary significantly. • Uniform and comprehensive approach for environmental impact of energy technologies. [ABSTRACT FROM AUTHOR]

• This study pioneers the use of deep learning in transforming Measurement and Verification (M&V) within industrial buildings, significantly curtailing the reliance on prolonged data collection periods, and setting a new standard for efficient energy management. • Our findings elucidate the impact of dataset size on M&V accuracy, showcasing that a compact, four-month dataset is sufficient for dependable energy savings verification, challenging the traditional norms of lengthy data collection. • Through a rigorous comparison of twelve methods, this paper underscores the superior performance of LASSO regression, Sequential Feature Selector, and Recursive Feature Elimination in refining model accuracy, marking a significant step forward in feature selection methodology. • Investigating various configurations, this research identifies five hidden layers as the ideal architecture for deep neural networks, optimizing the balance between model complexity and predictive precision in energy savings. • The study significantly contributes to the acceleration of the M&V process, enabling more rapid decision-making and implementation of energy conservation measures, thereby improving operational efficiency in industrial environments. • Highlighting the extensive implications for sustainable practices, this research illustrates how the integration of sophisticated analytics and deep learning can revolutionize energy management strategies, promoting a more sustainable approach to building operations. This study explores the potential of advancing industrial building energy Measurement and Verification (M&V) using Deep Learning techniques, with a focus on the impact of data size and feature selection methods on model performance. Traditional M&V practices in the industry require extensive datasets, typically spanning 24 months, to ensure accurate energy savings verification post energy conservation measures (ECMs). This research challenges the norm by investigating whether a condensed four-month dataset could suffice for reliable M&V, thereby accelerating the verification process which is crucial for industrial applications. Utilizing a dataset with 30-minute intervals, the study first applies multi-linear regression across twelve feature selection methods to identify the most effective techniques based on a comprehensive set of performance metrics including Training Time, RMSE, MAE, R-squared, CVRMSE, NMBE, Mean MSE (via k-fold cross-validation), and the Standard Deviation of MSE (over 10-fold cross-validation). The top three methods—LASSO regression, Sequential Feature Selector, and Recursive Feature Elimination—were further analyzed as input features for a Deep Neural Network (DNN) model. The DNN's performance was evaluated across varying data sizes (20 %, 40 %, 50 %, 60 %, 80 %, and 100 %) and configurations ranging from one to ten hidden layers. The findings reveal that LASSO regression, when integrated with DNN, consistently outperforms in terms of CVRMSE and NMBE metrics across different data sizes and model complexities. Notably, increasing the dataset size from 20 % to 80 % markedly improves the model's predictive accuracy, underscoring the significance of larger datasets in enhancing DNN generalization and performance. Additionally, the study highlights a critical trade-off in DNN architecture: while models with fewer hidden layers (1–3) show greater performance variability in smaller datasets, increasing the number of layers does not linearly translate to better performance, illustrating the nuanced balance between model complexity and efficacy. This research contributes to the energy M&V field by demonstrating that shorter duration datasets, previously considered insufficient, can indeed provide accurate energy savings verification when analyzed with advanced deep learning techniques. This advancement not only paves the way for quicker, more efficient M&V processes in industrial settings but also offers valuable insights into the optimization of DNN models for energy data analysis. [ABSTRACT FROM AUTHOR]

[Display omitted] • Dynamic properties highly dependent on energy storage capability of materials. • Internal temperature decreased from 33 to 27 °C due to developed panels. • The E-PCM performances were maximised by large daily temperature variations. • Decrement factor reduced to 0.42, time lag up to 3 h. • High thermal inertia of E-PCM suitable for summer insulation. In this work, the evaluation of E-PCM panels based on an elastomeric matrix containing a shape-stabilized paraffinic phase change material with a melting point of 28 °C was carried in a climatic chamber, simulating the summer daily temperature profile of four geographical locations in Italy at different latitudes. The characterization, carried out on testing boxes insulated using the E-PCM panels, allowed the determination of the delay (time lag) and of the dampening (decrement factor) in the propagation of the heat wave when passing from the outer to the inner surface of the walls; the results were correlated to two important parameters, the characteristic admittance and the heat capacity. From the difference between the heat fluxes on the outer and inner surface of the panels it was also possible to quantify the amount of stored energy and to do a comparison with a reference insulating panel (aerogel). Aerogel panels, despite the low thermal conductivity but due to the limited heat capacity and characteristic admittance, were unable to provide an effective insulation during summer conditions while E-PCM panels led to excellent outcomes. Results showed that with a peak of 33 °C in the external temperature, the phase change material limited the internal one at 27 °C with a maximum delay in the peak temperature of 6 h, an overall decrement factor of 0.4, time lag of 3 h and stored energy of 740 kJ/m2 per day. Finally, also the influence of the testing speed on the response of the E-PCM was evaluated through repetition of heating/cooling cycles in the temperature range 19–35 °C, highlighting the necessity of improving the thermal conductivity of the system in case of fast-response applications (cycle length < 3.5 h) and/or to optimize the PCM amount in the panel depending on the energy to be absorbed. [ABSTRACT FROM AUTHOR]

• The study clarifies the impacts of various transparent envelope thermal parameters on glazing surface temperature. • It provides optimum condition design guidelines for EAW in hot climates by investigating the thermal performance of DGEW and TGEW with different parameters. • The study examined EAW's thermal performance with varying air gaps, air velocity, return air temperature, and pressure values. • The outcomes add another level of validation to the experimental model by using the same CFD simulation to validate the experimental and mathematical models. Window systems, particularly in hot climates, are the source of considerable heat transfer that affects the buildings' indoor environment and energy consumption. Proper design and optimization of its thermal performance significantly contribute to the overall envelope energy performance. Using a multi-layer glazing system, Energy Active Window (EAW) adapts window insulation technology to reuse low-grade air from the HVAC system, keeping the temperature at the internal surface of the window close to the indoor air temperature, minimizing heat exchange between indoor and outdoor. This study aims to reduce energy consumption by optimizing EAW to increase its thermal resistance by channeling the return air (RA) from the HVAC system into the curtain frame, thereby lowering the temperature of the air gap and air film layer. The study examined the window system's exhaust/return air behavior using the Computational Fluid Dynamics (CFD) tool (Fluent) to simulate the heat exchange between outdoor and indoor building environments. The CFD simulation was validated using experimental measurements. Results show a surface temperature decrease of 4 to 7 °C based on the inner and outer pane airflow conditions when the outdoor temperature is 45 °C with an 8 % margin of error. Various EAW components were tested, including air velocity, air–gap width, volume, and outdoor temperatures, examining both triple and double-glazing layers. [ABSTRACT FROM AUTHOR]

Accurate occupancy prediction in smart buildings is crucial for optimizing energy management, improving occupant comfort, and effectively controlling building systems, particularly for short- and long-term horizons. Recently, deep learning-based occupancy prediction methods have gained considerable attention. However, the full potential of these methods remains under explored in terms of model architecture variations and prediction horizons. This study introduces cascaded LSTM and cascaded Bi-LSTM models for multi-horizon predictions from 10 minutes to 24 hours, integrating a modified activation function, additional input features, and optimized hyper-parameters using OPTUNA. Traditional performance metrics and various other analyses were conducted to compare the models. Both models performed well for short- and long-term predictions, with minimal differences in the results. Nevertheless, analysis focusing on non-zero data errors (accounting for approximately 11% of occupied periods) and occupancy-wise errors showed a significant performance gap between the two models. The cascaded Bi-LSTM model demonstrated consistent performance across various prediction horizons and occupancy variations, with accuracy approximately 10-15% higher than the cascaded LSTM model, highlighting its superior capability in capturing complex dataset dynamics through a bidirectional process. This study highlights the importance of additional input features, data feature analysis, and multi-perspective result analysis to select the most suitable model for occupancy prediction, validated with pre- and post-modeling feature importance analysis. • Cascaded LSTM and cascaded Bi-LSTM models for multi horizon prediction. • 10 minutes to 24 hours prediction added with a modified activation function. • Enhanced occupancy prediction with integrated features and optimized hyper-parameters. [ABSTRACT FROM AUTHOR]

The rapid urbanization and increasing use of distributed renewable energy resources have imposed a significant burden on power networks. Smart buildings equipped with artificial intelligence technology can play a pivotal role in energy management, ultimately enhancing energy efficiency and voltage quality. However, ensuring voltage stability within large-scale smart building systems presents challenges due to the coexistence of diverse energy sources and the fluctuating nature of renewable energy. This paper proposes a safe multi-energy management framework achieved by online decentralized execution and centralized training for large scale smart buildings in distribution networks. The energy management problem is formulated as a safety-augmented Markov decision process, presenting intractability for dynamic programming due to its extensive continuous state space. To solve this issue and improve the convergence speed and training process stability, a safety-augmented constrained multi-agent reinforcement learning algorithm based on reward extrapolation is proposed. In this algorithm, hazard values are introduced to enhance non-safe multi-agent reinforcement learning algorithms and meet safety constraints. A novel reward network is designed by imitating expert underlying intentions to ensure the rationality of the reward function for multi-objective tasks. Additionally, the loss function for estimating the Q-network is redesigned during training process to guarantee effective convergence. Theoretical analysis is conducted to provide the convergence guarantee. Numerical case studies based on actual data are performed to validate the effectiveness and scalability of our approach, showing that smart buildings can achieve superior energy management performance while ensuring voltage safety for distribution networks. The source code of the proposed algorithm is available at https://github.com/SYiyun/CMARL-EX. • A novel framework for online energy management in large-scale smart building systems is proposed. • The proposed method ensures voltage safety in online multi-agent reinforcement learning. • Redesigning the loss function's target of the estimation Q-network ensures enhanced task performance. • Imitating expert demonstrations improves stability and accelerates learning in reinforcement learning-based smart buildings. [ABSTRACT FROM AUTHOR]

• Assessment of the energy performance of mixed-mode ventilation in office buildings. • Evaluation of energy performance in archetypes according to climate change impacts. • Natural ventilation reduces energy consumption in office buildings over time. • Adaptive strategies needed in office buildings to mitigate climate change impacts. • Belo Horizonte is more vulnerable city to an increased in energy consumption. With the projected increase in temperature, coupled with the rise in intensity and frequency of heatwaves caused by climate change, it has become essential to investigate the impact of climate change on building energy consumption and thermal comfort. This study focuses on office buildings with mixed-mode ventilation in Brazil as a case study. Using a typical 15-story office building archetype for various Brazilian cities, considering current and predicted scenarios (2050 and 2080), the potential for energy savings through natural ventilation was assessed. It was observed that the average increase in temperature and wind speed in the predicted climate varied in each city and showed significant variations over the years. Predicted projections under high-emission scenarios (SSP5-8.5-2080) reveal significant increases in energy consumption, highlighting the vulnerability of cities like Belo Horizonte, which could face a 77% increase in total energy consumption. Additionally, the analysis of HVAC hours of use over time indicates a progressive increase, suggesting a potential reduction in natural ventilation as a mitigation strategy. Mitigation measures, especially in the SSP1-2.6 scenario, are crucial to stabilize HVAC consumption and preserve the use of natural ventilation, emphasizing the importance of sustainable approaches to address the energy challenges arising from climate change. [ABSTRACT FROM AUTHOR]

The demand side is required to increase its energy flexibility to tackle the demand–supply mismatch caused by integration of renewable energy sources into the energy mix. Model Predictive Control (MPC) is one of the promising measures for achieving this goal. MPC requires operational data from the building to train its predictive model. The quality of this data affects the performance of MPC. This paper aims to analyze the impact of a dataset's size and excitation method on flexibility harnessed by MPC. Five datasets have been generated that differ in their size and excitation method. They are used to train three modeling algorithms on two buildings. Next, generated predictive models are integrated into an MPC framework, to assess the impact of datasets on the performance of MPCs for activating the energy flexibility of a building. The case studies are two similar dwellings where one is equipped with Electric Heaters (EH), and the other uses an UnderFloor Heating (UFH) system. Results show that the flexibility of the EH case is more sensitive to the size of the dataset. Furthermore, it is shown that the suitability of the excitation signal for flexibility activation depends on the modeling algorithm. The results show that choosing an ill-suited dataset might cost the MPC 35 % and 20 % of the potential flexibility for EH and UFH cases, respectively. [ABSTRACT FROM AUTHOR]

The data-driven approach currently dominates the field of Automatic Fault Detection and Diagnosis (AFDD) in HVAC systems. However, a significant concern lies in the prevalent use of labeled experimental and simulation data, which often does not represent real-world operational conditions. This study unveils a comprehensive framework for AFDD in light commercial buildings, effectively leveraging unlabeled raw data extracted directly from their Building BAS. Its main goal is to provide a versatile methodology tailored for real-world applicability. Buildings classified as "light commercial" typically have less than 2,500 square meters of floor area and no more than six stories, such as small offices, medical facilities, banks, small manufacturing facilities, etc. A common feature of these buildings is the fact that the HVAC systems tend to be relatively simple and have similar configurations, thus making it easy to develop scalable and reproducible fault detection methods. This paper focuses on a practical case study in a light commercial building HVAC system situated in Montreal, Canada, encompassing a single Air Handling Unit (AHU) and four Variable Air Volume (VAV) reheating boxes to evaluate the framework. This comprehensive framework encompasses a sequence of sub-objectives: creating a sizable, synchronized raw dataset from diverse BAS sensor tags, comprehensive data cleansing to address inconsistencies, developing an anomaly detection method, investigating these anomalies to extract underlying rules, and finally, dataset labeling. An AFDD classification model is then applied to evaluate its ability to distinguish normal from faulty conditions across various fault types. The study highlights the potential of dimensional reduction techniques and unsupervised clustering for effective anomaly detection in light commercial buildings, as well as the power of the Decision Tree classifier for uncovering hidden patterns, especially in anomaly conditions. It also highlights the significance of addressing imbalanced datasets in AFDD and the complexities of detecting sizing-related faults. Despite these challenges, the framework exhibits robust performance in detecting and diagnosing a range of HVAC faults. It offers a systematic and adaptable approach for handling real-world operational data in light commercial building HVAC systems, extendible to other building types, bridging the gap between data-driven methods and practical applications. [ABSTRACT FROM AUTHOR]

• Heat load shifting in a real-life school building is demonstrated over a six-weeks period. • Schedule-based control efficiently shifts loads to off-peak periods, enhancing flexibility without sacrificing efficiency. • Pupil survey on thermal comfort perception shows no significant difference during the test period compared to business-as-usual operation. • Practical challenges in integrating simplified control approaches for building energy flexibility are outlined. This work investigates the potential of simplified control approaches to deploy the building energy flexibility (BEF), here for the shifting of the space-heating load in a real-life educational building. The educational building is a passive house school where internal gains play an important role in the room thermal dynamics. It is equipped with a waterborne heat distribution system connected to district heating. The building is located in Elverum, Norway, having a strong heating-dominated climate. Focusing on schedule-based control strategies for pre-heating the building in the mornings, the study demonstrates significant load shifting to off-peak hours. The energy use during typical peak hours (7a.m. to 9a.m.) is reduced by 50% while the daily energy use is not increased significantly, highlighting the effectiveness of this simple approach. Occupant acceptance surveys among the pupils reveal no significant differences in thermal comfort perception between the periods with business-as-usual and schedule-based controls. Practical challenges in integrating simplified controls are highlighted and underscore the importance of considering energy flexibility during the building tendering and design phase. Bridging the gap between theoretical research and real-life applications, this research contributes to the advancement of energy-flexible operation of real-life buildings. [ABSTRACT FROM AUTHOR]

Buildings have the potential to mitigate climate change effects by integrating energy-efficient solutions. Building resilient design strategies based on forthcoming weather predictions offers an effective means of adaptation. The study focuses on the impact of climate change on residential buildings in Istanbul and Izmir, two Turkish cities with distinctive climate characteristics. By creating future weather scenarios and conducting dynamic simulations, buildings' and improvement measures' performance under RCP 4.5 and RCP 8.5 climate scenarios and short- and long-term periods are evaluated. The findings reveal varying degrees of climate change impact on the two regions, with decreased heating degree days (HDDs) and increased cooling degree days (CDDs). Notably, the RCP 8.5 scenario projects significant temperature increases, with a rise of 4.3 °C in Istanbul and 5 °C in Izmir, leading to profound consequences for buildings. The CDD can be doubled in July and reach 292 in Izmir and quadrupled, reaching 158 in Istanbul. Without retrofit, in a well-naturally ventilated building, primary heating energy consumption can be decreased by 36–41 %, while primary cooling energy consumption tripled in both cities. With the aid of improvements, a ∼ 5–6 °C decrease was observed in the highest temperature predictions in naturally ventilated spaces in summer in Istanbul, while a ∼ 4–5 °C decrease was observed in Izmir. [ABSTRACT FROM AUTHOR]

• Impact of practical challenges on the choice of a scalable data-driven approach to control the operation of a building heating system in a predictive manner considering 300 + zones in a real-life office building is presented. • More than 15 valuable lesson-learned from the experience of developing and implementing data-driven predictive control for building operation are presented. • 24 practical challenges are categorized and linked to the different building project/lifecycle phases to emphasize the importance of considering data-driven services during early phases of building projects. Data-driven applications in buildings using AI and machine learning have generated a lot of interest, but scaling these applications is challenging due to the uniqueness of each building. During the process of implementing a data-driven predictive heating control in a full-scale real-life office building in Norway, 24 practical challenges were encountered. In this work, those practical challenges are presented, discussed and attributed to four main categories: i) physical limitations, ii) data acquisition and communication, iii) data and model definition and iv) building occupants. Detailed examples for the challenges are provided and more than 15 lessons-learned with regards to developing and implementing data-driven services for building operation are presented. Furthermore, this work discusses how the practical challenges impact the choice of a data-driven approach to control the operation of an office building heating system in a predictive manner and how the practical challenges influence the creation of variation in the measurement data needed to identify a model during normal building operation. Finally, it is shown that a substantial number of practical challenges that were encountered during the operational phase are rooted in the design and construction phase of a building project or from rehabilitation during the operational phase. This highlights the fact that the possible use of data-driven services for building operation should be considered during the tendering and design phase to minimize the number of challenges regarding the widespread implementation of data-driven services for building operation, especially regarding predictive control. [ABSTRACT FROM AUTHOR]

Nearly-Zero-Energy and Zero-Emission Buildings are one fundamental part of the world strategies addressed at mitigating the global warming trend and coping with the goal of setting 1.5 °C the planet temperature increase with respect to the pre-industrial conditions. The present paper refers to a specific case study, the Smart Energy Building (SEB) located in the Savona Campus of the University of Genoa. The SEB is a very innovative building for both the envelope (ventilated highly insulated facades) and the energy systems (including a ground coupled heat pump and an exhaust air-to-air heat pump for building ventilation); the building is equipped also with a PV module field on the rooftop for electricity production. The present paper first evaluates the heating and cooling loads of the building by means of an EnergyPlus model and analyses the impact of different control strategies related to the temperature setpoints. In particular, case #1 refers to a minimum temperature to be assured by heating only in winter and to a maximum one to be avoided by cooling only during summer; on the other hand, in case #2 the temperature inside the building is controlled within a defined range (both heating and cooling modes) all year long. From EnergyPlus simulations, the hourly heat loads have been averaged as monthly values and employed for the calculation of the required overall length of the ground heat exchangers of the geothermal heat pump of the SEB building. The ground-side analysis is performed by applying the Authors' ASHRAE- T p8 method, in its recently released web version. For the BHE (borehole heat exchanger) calculations, a parametric analysis has been performed in terms of ground thermal conductivities. Finally, the PV electrical production has been estimated by EnergyPlus simulations and compared with the corresponding measured one, thus showing the net energy zero behaviour of the present building. [ABSTRACT FROM AUTHOR]

With the forecast of a warmer world, with longer, more frequent and more intense heat waves, it becomes essential to have buildings that are resilient to a rapidly warming climate and able to withstand extreme temperature events. This research investigates the concept of buildings' resilience. Specifically, the article collects definitions of resilient building found in 32 scientific articles referring primarily to thermal aspects and seeks to associate each definition with one of the two interpretations of resilience, namely the engineering and ecological ones, mainly identified within the theory on resilience. This aimed to assess whether there is a predominant and – more importantly – a clear understanding of resilience for buildings, to which extent this concept is well defined and clear, which is a topic that, to the best of our knowledge, no study on thermal resilience of buildings seems to have examined to date. Results showed a significant lack of consensus regarding the interpretation of buildings' resilience within the selected literature. Such lack of consensus highlights the ongoing confusion about the concept of buildings' resilience to the threats posed by the current and future climate; what should this concept include concretely? A certain discrepancy also emerged even between the interpretation used to define resilience and that used for its indicators. Furthermore, the available definitions are observed to be rather generic, not specific to buildings and, in any case, it does not seem easy to make them operative. In addition to the literature analysis, the article proposes a tentative approach, which could usefully arrive at an attempted definition of resilience that is specific to buildings and operative. Starting with buildings, the proposed "bottom-up" approach is innovative compared to the prevailing approach in the literature that starts with definitions of ecological and engineering resilience (top-down) instead. Furthermore, the article delivers a couple of considerations to be accounted for when applying the concept of resilience to buildings: the importance of considering not only extreme events but also climate change understood as an increase in average air temperature and the importance of also including the inherent resilience due to the presence of occupants, whose thermal adaptive behavior can mitigate the risk of power outages by making the building less vulnerable to the impacts of climate change. By providing an operational tool for researchers, engineers and planners who intend to strengthen the resilience of the building in the face of climate change, the research contributes to achieving a clearer and shared understanding of how the concept of buildings' resilience should be declined. [ABSTRACT FROM AUTHOR]

[Display omitted] • Carbon neutrality feasibility of buildings was reviewed. • Carbon neutrality of buildings is feasible, but complexity varies. • Multi-building carbon neutrality case studies are limited. • Climate type affects neutrality; high-rise research is deficient. • Long-term lifecycle impacts of carbon measures remain unclear. Achieving carbon–neutral building stock by 2050 contributes to coping with the detrimental impacts of global warming since buildings account for almost 37% of final energy-related CO 2 emissions. This paper reviews the publications on carbon neutrality (CN) feasibility at both single and multi-building complex scales. Publications are retrieved from the Scopus database, and the snowball method is used to find the most relevant studies. The paper addresses the primary question: "Is it possible to reach the life cycle carbon neutrality of buildings?". Various information such as building life cycle carbon assessment, building characteristics (usage type and number of stories), climate type, mitigation measures, and simulation results are extracted, classified, and analyzed. Technically, reaching CN is feasible, but it is challenging given the need to implement multiple mitigation measures simultaneously, especially in the regions with low emissions intensity of the electricity grid, and may not always be economically feasible. A lack of successful case studies for large multi-building complexes, such as cities, is evident in the literature. It can be attributed to the limited availability of input data for carbon assessments as well as the complexity of simulations. Knowledge gaps in the literature and suggestions for future works are also discussed in detail. Due to the small number of studies on the topic, conclusive paths to reach the CN of buildings, specific to different climates and types of buildings, remain unclear, and thus, further research is necessary. [ABSTRACT FROM AUTHOR]

This work presents a new operational framework to measure the smartness and smart readiness of highly electrified buildings. The framework seeks to enhance legacy systems and controls of existing buildings and establish minimum criteria for future constructions to ensure they interact effectively with users and the grid, aiming for a clean energy transition. To this end, we develop two modified complementary assessments, one based on the method indicated by the Smart Readiness Indicator (SRI), proposed by the European Union (EU), and the other following the Smart by Powerhouse scheme, introduced by a Norwegian consortium of stakeholders focused on developing future proof climate buildings. The proposed structure is implemented in ten non-residential buildings in Norway with different energy systems, typologies, and construction dates. The results of this study demonstrate that energy flexibility quantification plays a crucial role in correctly implementing the framework in highly electrified buildings. Therefore, the dynamic impact of having Electric Vehicle Charging (EVC) and other electrical-dependent loads must be considered in the assessment. With the proposed modifications, the EVC weight in the flexibility score now varies from 24.0 to 43.6%, up from the original 5%. Overall, the pilot buildings have a smart readiness level between 21.6% and 31.7%, with mostly automated smartness levels. Nevertheless, the study also emphasizes the need to differentiate current HVAC (Heating, Ventilation, and Air Conditioning) technologies and their efficiencies. [ABSTRACT FROM AUTHOR]

Resilience is becoming increasingly important in power systems, microgrids (MG) and buildings. It evaluates their performance against disruptive events. The approaches mainly correspond to high-impact, low-probability events such as natural disasters and intentional attacks affecting the electrical systems infrastructure. However, resilience can encompass medium and low-impact events such as minor civil structural accidents, light faults and supply disturbances. Some resilience assessment advances are in buildings at the low-voltage (LV) level. They include vulnerability to natural disasters, reliability of supply, and service quality. These works usually use approaches independent of each other, leaving a gap between their relationship and interpretation. Therefore, there is a need to consolidate a resilience assessment strategy to guide the analysis of vulnerabilities and strengths in the same direction. This paper proposes a comprehensive approach to evaluate electrical resilience for buildings. It compiles quantitative strategies for studying electrical resilience, focusing on LV systems. It proposes a methodology for integrating the electrical system infrastructure's vulnerability, the supply's continuity and the voltage service's quality. Implementing this approach in a university building equipped with smart metering demonstrates the effectiveness of the proposed methodology for assessing electrical resilience. The results show a comprehensive resilience analysis and the possibility of extending the methodology to MG and LV distribution networks. • A categorisation of building electrical system disturbances by impact level and frequency. • A methodology to assess electrical resilience for each category of disturbance. • A Comprehensive assessment of electrical resilience, covering structural robustness, service continuity, and supply quality. • A case study application integrating critical loads, a backup system, and smart metering. [ABSTRACT FROM AUTHOR]

• The study obtained that net positive energy generation for three different types of academic buildings in Bangladesh. • All studied cases were found feasible based on economic matrices i.e LCOE, NPV, PBY, PI. • The installation of PV systems at academic buildings resulted in a significant amount of carbon emissions reduction. • Alignment of sunlight and peak load hours of academic buildings presents a viable option to achieve net zero energy. Bangladesh faces challenges with global carbon emissions and energy security due to its heavy reliance on imported fossil fuels. This paper investigates the techno-economic viability of photovoltaic (PV) systems for achieving net-zero energy in academic buildings as a sustainable alternative. The study evaluates the performance of PV systems based on factors including annual energy production, capacity factor, net-zero energy potential, self-consumption/sufficiency ratio, renewable fraction, levelized cost of energy (LCOE), net present value (NPV), payback period, profitability index (PI), and carbon emission reduction. The results indicate that optimally sized PV systems can generate net positive energy: 6.18 MWh for universities, 4.04 MWh for high schools, and 3.62 MWh for primary schools. Economic viability was assessed under two billing methods (net metering and all energy buy/sell), and all cases showed positive NPV and PI values greater than 1. Decarbonization analysis revealed significant reductions in CO 2 emissions: 2026.5 tons for universities, 18.9 tons for high schools, and 9.7 tons for primary schools. Finally, a sensitivity analysis assessed the optimal PV system size for seven other regions in Bangladesh. These findings suggest that the widespread adoption of PV systems in academic buildings can play a crucial role in achieving net-zero energy, contributing significantly to the government and policymakers' pursuit of sustainable development goals. [ABSTRACT FROM AUTHOR]

Achieving carbon neutrality by 2050 has become a global goal, sparking concerns regarding energy consumption and carbon emissions in building operations. Office buildings in high-rise high-density cities serve as central business districts, contributing significantly to the city's economic activity and consuming a lot of energy. The process of retrofitting existing office buildings for energy efficiency in high-rise high-density cities tends to be challenging. However, there is a lack of comprehensive understanding of the challenges involved in office buildings' energy retrofitting, as they have not been thoroughly explored. This study aims to investigate the challenges to the existing office building energy retrofitting (EOBER) in high-rise high-density cities with real cases in Hong Kong. Initially, a systematic literature review was undertaken to identify 49 potential EOBER challenges and categorized into seven groups: technical, financial, institutional, social, environmental, regulatory, and other categories. Afterward, 23 EOBER challenges were identified through 24 semi-structured interviews with 36 real office building energy retrofitting cases in Hong Kong. Moreover, these challenges were quantified by the Z-numbers-based Delphi survey and analysis. Results show that regulatory challenges are the primary challenges, followed by financial challenges. The lack of government incentives, policies, legislation and regulations significantly hinders practitioners' ability to engage in energy retrofitting initiatives. The long payback period of building energy retrofitting poses a critical financial concern for practitioners embracing such initiatives. In the end, this research proposed integrated strategies to tackle these challenges and increase building energy efficiency, including launching financial and regulatory incentives, shortening the interval for mandatory energy audits, disseminating knowledge, and diversifying finance channels of building energy retrofitting. The findings contribute to the body of knowledge by employing systems thinking to identify and evaluate EOBER challenges in high-rise high-density cities through empirical methodologies. Moreover, this study provides valuable references for practitioners in navigating these challenges and minimizing risks associated with the retrofitting process. [ABSTRACT FROM AUTHOR]

• Energy performance of HVACs in super high-rise buildings were field-tested and analyzed. • The representative issues leading to large energy consumption of HVACs were summarized. • Commissioning of VAV system and multistage chilled water systems were conducted. • The setting values of HVACs should be adjusted in time according to practical cooling demand. The HVACs in super high-rise buildings commonly consists of variable air volume (VAV) systems, multistage chilled and cooling water systems, primary-secondary chilled water system in chiller plant, and the chillers combination is much more complex, leading to the significantly higher energy consumption than that of normal buildings. This paper contains field tests and comparative studies on the operation performance of HVACs in two super high-rise buildings and summarized representative issues accounting for the high energy consumption. Field test results showed that the annual energy efficiency of the whole HVAC system, before being commissioned, was only 1.79 and 2.15 in two projects. The HVACs, typically VAV systems, chilled and cooling water systems, all suffered from over-supplying and energy wasting. Besides, the chillers commonly operated with a low partial load ratio (PLR), whose energy performance was much lower than expected. Aiming to dig out the typical issues, analyses were carried out from external, internal factors, and their interconnection. Furthermore, optimization methods were put forward with corrected vital indexes. Results showed that proper control strategies should maintain the VAV system, multistage chilled and cooling water system operating with significant distribution temperature differences, tiny distribution pressure drops, and high energy efficiency, thus achieving energy saving and ensuring space cooling effects. Besides, the control strategies, especially the number control of chillers, were influential during the whole cooling season to ensure the chillers operate with optimal PLR and thus achieve better energy performance. [ABSTRACT FROM AUTHOR]

• A pre-1919 primary school with single glazing in Edinburgh has an oversizing of its space heating system of 32 % at design conditions. • Supply temperature can be operated below 70 °C all year round. • Supply temperatures can be reduced below 55 °C all year-round during daytime. As part of the decarbonization of its existing building stock the UK needs to replace gas boilers with heat pumps and low-temperature heat networks. Both technologies are efficient at supply temperatures of 55 °C, with a maximum of 70 °C for heat networks, when existing systems have been designed to operate at 82 °C. Most space heating systems are expected oversized and operate most of the year in part-load, which facilitate the temperature reduction, but little is known about the degree of oversizing. This paper uses the case study of a pre-1919 school in Edinburgh to evaluate the true oversizing of its heating system and its consequences on minimum supply temperatures. This building is still equipped with single glazing and is therefore a worst-case scenario. An oversizing of 32 % was estimated which enables the use of a supply temperature below 70 °C all year round. Moreover, the supply temperature can be kept below 55 °C all year round during daytime due to internal heat gains. In conclusion, this building shows a nascent readiness for low-temperature heat. It would benefit from energy efficiency measures such as double-glazed windows, but this is not pre-requisite. Those results are important for the heat network industry when sizing their systems, but also energy and asset managers when they schedule renovation programmes to have their buildings low-temperature-ready. [ABSTRACT FROM AUTHOR]

• Investigated energy storage in commercial buildings with PV systems. • Hierarchical analysis approach to identify optimal BESS capacity. • Results indicate medium offices, warehouses, and schools need BESS. • Recommends charging time windows for optimized BESS use. • Cost recovery period ranges from approximately 2.5 to 8.2 years. The increasing energy demand in commercial buildings has led to the adoption of photovoltaic (PV) systems as a viable solution. However, the gap between energy consumption and PV capacity in commercial buildings remains uncertain, and there is limited research on the potential for energy storage in different commercial building types. This study utilizes Gaussian mixture modeling to extract energy consumption and PV capacity patterns from diverse commercial buildings, using datasets provided by the Department of Energy. By employing hierarchical analysis, we investigate the energy storage potential of various commercial buildings based on the disparities between energy consumption and PV capacity patterns. Moreover, this study introduces innovative battery energy storage system (BESS) prototypes tailored to the specific needs of different commercial building types. These prototypes determine the optimal BESS capacity and charging schedules, providing an effective means of improving electrical resilience in commercial buildings. The findings reveal that medium offices, warehouses, and secondary schools require BESS capacities of 410kWh, 838kWh, and 349kWh, respectively, and recommend charging time windows of 9:00–17:00, 8:00–18:00, and 9:00–14:00, respectively. Additionally, the cost recovery period for Li-ion phosphate and ternary Li-ion batteries ranges from approximately 2.5 to 7.5 years and 4 to 8.2 years, respectively. The developed BESS prototypes based on energy storage potential offer resilience to commercial buildings during power outages caused by natural disasters and emergencies, helping to improve their electrical resilience. This study provides valuable insights into the potential for energy storage in commercial buildings and promotes the wider implementation of BESS in the commercial sector, contributing to the achievement of effective demand-side management. [ABSTRACT FROM AUTHOR]

The renovation of existing energy-inefficient residential buildings is an important measure to improve building energy efficiency and achieve CO 2 emission reduction strategic tasks. At present, existing energy-inefficient residential buildings suffer from high energy consumption for heating, low efficiency, and poor indoor thermal comfort. It is necessary to implement energy-saving renovation on existing residential buildings and evaluate the renovation effect. This article took existing energy-inefficient residential buildings as the research object and selected a targeted evaluation system. Based on expert research, the hierarchical-grey correlation degree method was used to determine the weight of indices. Actual testing was conducted on the three nodes of the heat exchange station, secondary network, and the building ontology during the heating seasons of 2015–2016 and 2016–2017 (before and after renovation). Based on measured data, a fuzzy membership degree evaluation model was constructed, and the evaluation results were determined using a linear weighted method to form a complete evaluation system. The evaluation system covered 12 evaluation indices, and the evaluation indices with the largest weight in the criterion and index layer were energy consumption (0.308) and economic contribution rate (0.183), respectively. The average linear weighted evaluation scores before and after the renovation of each node were 14.16 and 29.04, respectively. [ABSTRACT FROM AUTHOR]

Standardized replicable buildings can cause discomfort and increased air conditioning usage. Besides, super cool coatings are effective passive solutions. Therefore, this study evaluates thermal comfort in a standardized Brazilian public school in 8 climates, verifies suitable super cool coatings and identifies optimal additional passive techniques to meet regulatory requirements without active air conditioning, a topic worthy of investigation. Methods include pilot simulations with DesignBuilder, thermal comfort analysis considering ASHRAE 55 adaptive model; JEA Algorithm optimization simulations; identification of most influential passive strategies by sensitivity analyses; and evaluation of the ideal opaque envelope through heat balance. Results showed that current model do not meet the Brazilian normative. Thus, different super cool coatings are recommended: thermochromic for heating-dominated; spectrally selective for cooling-dominated dry and high broadband emissivity for extreme-cooling-dominated humid zones. Regarding opaque envelope, thermal mass flat roofs and insulated walls are advised. In 7 of 8 climates, super cool roof was most effective followed by natural ventilation. Optimal combinations include super cool envelope, window shading and natural ventilation 24 hours 7 days a week with 95 % of free-aperture for extreme-cooling-dominated zones; super cool roofs, medium reflectivity walls and 5 % of free-aperture for cooling-dominated; thermochromic roof and dark walls with natural ventilation during occupancy with 5 % of free-aperture for heating-dominated. [ABSTRACT FROM AUTHOR]