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• This paper presents a comprehensive review of cross ventilation research in buildings. • The advantages and disadvantages of different research methods are compared. • The ventilation models of cross ventilation are reviewed. • The influencing parameters of cross ventilation are summarized. • The future perspectives of cross ventilation research and practical applications are proposed. There is a growing body of literature that recognizes that natural ventilation plays a vital role in indoor air quality, thermal comfort and building energy consumption. This paper systematically reviews the previously published research of the most efficient and typical natural ventilation type - cross ventilation, aiming to present the main research topics in contemporary research and provide an agenda for future studies. The methodologies, airflow pattern, ventilation models and influencing parameters of cross ventilation were comprehensively summarized and discussed. The chained analysis and data-driven methods are the potential approaches to study cross ventilation more efficiently. The comparisons of different ventilation models of cross ventilation help to better understand the basic mechanisms that drive the cross ventilation airflow. [ABSTRACT FROM AUTHOR]

Diagrams of (A)conventional cool roofs, (B) super cool roofs, and (C) temperature-adaptive roofs whose color can change with temperature. [Display omitted] • Cool roofs are divided into conventional cool roofs, super cool roofs, and temperature adaptive roofs. • The use of cool roofs is limited by geography and climate, with the net cooling power of up to 150 W/m2 in dry, rainless, and clear sky areas. • The use of cool roofs in cities may cause light pollution problems, which can be alleviated by special structures or materials. • Not only does atmospheric water vapor affect the radiative cooling power of cool roofs, but in turn, cool roofs can increase the water vapor content above the roof. • The large-scale use of super cool roofs may affect the urban wind field. Cool roofs play a significant role in mitigating urban heat islands, improving indoor thermal comfort, and saving energy. In recent years, with advances in the manufacturing of nanophotonics and metamaterials, researchers have developed super cool roofs where the surface temperature remains below the air temperature in direct daylight and temperature-adaptive roofs where the solar reflectance or thermal emissivity can change with temperature. This paper reviews the research progress and status of conventional cool roofs, super cool roofs, and temperature-adaptive roofs. This paper affirms the role of cool roofs in mitigating urban heat islands and energy conservation. And this paper also summarizes some of the crucial issues that cool roofs may face when used in cities. The effects of cool roofs on urban wind fields, planetary boundary layer heights, and pollutants above cities as well as the effects of sky view factor, atmospheric humidity, dust, and aging on the performance of cool roofs are discussed. The results show that the use of cool roofs is limited by geography and climate. The net cooling power can reach 150 W/m2 in dry, rainless, and clear sky areas. Cool roof technology is less effective in hot and humid climates because the first atmospheric window is affected to varying degrees by the increased radiation medium in the atmosphere, while the second atmospheric window is nearly closed in hot and humid climates, weakening the terrestrial long-wave radiation entering space. The use of cool roofs in warm and humid climates (over 80% relative humidity, with temperature over 24 °C) for most summer nights may limit the radiative cooling performance of the cool roof. The large-scale use of cool roofs in cities near huge lakes or seas may affect the urban wind field, causing a cooling island effect and a local build-up of pollutants. Finally, an outlook on the research prospects of cool roofs was given to provide ideas for further research. [ABSTRACT FROM AUTHOR]

The accuracy of power load forecasting plays an important role in the development of the economy and the promotion of energy consumption and transformation. Aiming at the strong fluctuation and variability mechanism of electricity consumption, this paper proposes a novel hybrid method with the Long Short-term Memory (LSTM) on the dual optimization of particle swarm algorithm and butterfly algorithm, namely the EWT-MOLSTM-SVR model. Adaptive signal decomposition Empirical Wavelet Transform (EWT) decomposes the fluctuating electricity data into different modes, and uses the frequency domain analysis of signal analysis to classify and forecast. LSTM alone reveals that the advantage of the data variability mechanism is not significant. Particle Swarm Optimization (PSO) is used for law control, and Butterfly Optimization Algorithm (BOA) is used for parameter promotion to jointly optimize the expression effect. The PSO optimizes the LSTM by improving the convergence speed and global search ability of the BOA, and the optimal hyperparameters could be searched for forecast so that the accuracy of the forecast was improved. This paper adopts the data of American residential electricity data for empirical analysis. The experimental results show that the model is significantly better than Recurrent Neural Network (RNN), General Regression Neural Network (GRNN), PSO-SVM, and other models, which has a good effect on improving the forecast accuracy. The hybrid model reveals the fluctuation mechanism and variability of electricity data, deeply analyzes the economic behavior of electricity, and provides a new method for exploring residential energy consumption and optimizing residential buildings. [ABSTRACT FROM AUTHOR]

• The paper reviews 33 on-field residential feedback studies. Only occupied homes where feedback was provided and energy consumption was monitored were considered for the review. • Our study proposes a taxonomy for energy feedback based on different characteristics of feedback such as frequency, type, presentation style and mode of access. • Understand the different ways in which field studies were conducted and assess a few parameters to establish their relationship with energy savings achieved. Residential energy feedback is about providing personalized information on household energy use to consumers to encourage energy savings. This paper conducts a review of field-based studies that have evaluated the impact of energy feedback on residential energy consumption. The review includes studies in real occupied homes that have deployed feedback intervention(s) and measured energy savings. Our study builds a taxonomy for energy feedback based on different characteristics of feedback such as frequency, type, presentation style, and methods of access. Energy savings from similar feedback types were found to differ depending on how the study was conducted. The reviewed studies deployed a range of feedback information including energy units, energy cost and tailored information conducted across diverse audiences (ethnicity, geographical positioning), varying experimental types (longitudinal, Randomized Control Trial) and, size and duration of the studies. The duration of studies varied widely, ranging from one month to three years and demonstrated energy savings between 5% and 20%. While most studies achieved energy savings due to energy feedback, a few of them reported an increase in energy consumption which could be due to rebound effect. Most of the studies in literature adopted a longitudinal design where the current household energy consumption was compared with the past consumption to arrive at the savings. Others used Randomized Control Trial (RCT) design where energy consumption of intervention group was compared with control group. Most of the studies were conducted in developed countries with cold climates.. There are fewer studies done on large-scale residential energy feedback especially in emerging economies where there is a rise in air conditioning use. We propose future studies to also consider the human behavior and cultural influences while evaluating impact of energy feedback. Our recommendation is that the academic and policy community address this gap since energy feedback is likely to stimulate positive behavior change amongst householders leading to energy savings. [ABSTRACT FROM AUTHOR]

This paper focuses on how the existing sealed office typology can adapt to a warming world while minimizing the high energy demand for space cooling, through Dynamic Cooling , by adjusting the cooling temperature profile towards the average human circadian rhythm to mitigate heat stress during times of heightened thermal sensitivity. This concept is explored through a study in which we compared the effects of Dynamic Cooling , constant cooling, and no cooling on the core body temperature of 21 participants as well as a thermal dynamic simulation of 270 office space configurations under the same cooling profiles, comparing the total energy demand, peak loads, and overall hours of discomfort. The results of the study suggest the different conditioning profiles have no direct impact on the overall change in core body temperature throughout the day (difference < 0.04 K) but do influence the overall range throughout (from 0.37 K under constant cooling to 0.53 K under Dynamic Cooling). Additionally, there is no clear correlation between elevated indoor temperatures and CBT during the morning hours, questioning the necessity and effectiveness of pre-lunch cooling. This is underlined by the simulation findings, which reveal a non-linear relationship between savings in cooling demand and a decline in thermal comfort, with a 51.3 % reduction in ideal cooling energy demand and a 25.2 %–36 % increase of Over-Temperature-Kelvin-Hours, from a steady state cooling profile to a profile of Dynamic Cooling. The results of the study and simulation challenge traditional cooling standards and highlight the need for a balance between minimizing thermal discomfort and the necessary cooling energy demand. [ABSTRACT FROM AUTHOR]

The field of sustainable construction and interior design demands innovative solutions to optimize energy efficiency and minimize environmental impact. This need has been further amplified by the European Community's Directive on Energy Efficiency 2012/27/EU and its subsequent revisions. The kitchen, as a central hub of domestic energy use, presents a significant opportunity for improvement. This paper introduces a novel application of heat pump technology for energy reuse in household appliances. The proposed SMACK (SMArt energy-Conserving Kitchen) system embodies a holistic approach, merging eco-sustainability with advanced design principles. SMACK's modularity enables customization and scalability to meet diverse requirements. A comprehensive assessment indicates potential average energy savings approaching 50%, contingent upon appliance configuration. Importantly, this study incorporates preliminary real-world measurements on a minimal appliance, offering empirical insights into the SMACK system's performance. The SMACK project signifies a major step towards environmentally conscious design, setting a new standard for sustainable technology integration within the home. [ABSTRACT FROM AUTHOR]

Accurate forecasting of building cooling load is essential for optimizing control strategies and energy efficiency in heating, ventilation, and air conditioning (HVAC) systems. However, existing machine learning and deep learning algorithms encounter challenges in addressing the dynamics and uncertain nature of cooling load demand, as they solely focus on temporal analysis. To tackle this issue, the paper proposes CRG-Informer, a deep learning approach that integrates spatio-temporal features to enhance multi-step predictive performance. Firstly, a time series spatial control relationship graph set is constructed based on the interconnected structure, static attributes, and control characteristics of the cooling system. Then, a graph neural network (GNN) is designed for graph embedding to extract spatial correlation information at each instance. Additionally, the Informer model with the ProbSparse self-attention mechanism is utilized to capture long-term temporal dependencies among input sequences. This integration of components enables feature fusion, and the proposed approach is evaluated using data from an office building in Hangzhou, China, over an entire cooling season. Extensive experiments were conducted to compare our method with five superior baseline models for 6, 12, 24, 72, and 144-step cooling load forecasting across four types of data acquisition intervals. The results demonstrate that the CRG-Informer, through spatio-temporal feature fusion, outperforms existing methods and provides a novel solution for improving cooling load forecasting performance. • Introduced spatio-temporal feature fusion concept in cooling load forecasting. • Developed a graph modeling and embedding method for HVAC cooling system. • Employed the novel deep learning model Informer for cooling load forecasting. • Validated the superiority of CRG-Informer over five baseline models in real tasks. • Extensive experiments providing valuable references for research and application. [ABSTRACT FROM AUTHOR]

[Display omitted] • Passive, active and renewable ECMs under climate change are reviewed. • Insulation and window design are common ECMs, yet trends in research suggest a growing emphasis on potential strategy shifts to cooling techniques, photovoltaics and heat pumps. • ECMs should be tailored to specific climates, building types and cost-effectiveness. • Challenges of short-term gains versus long-term effectiveness of ECMs are discussed. • Embracing life cycle thinking, resilient design under uncertainty, and frequent building code revisions is crucial for climate change mitigation. Energy conservation measures (ECMs) are often chosen for their immediate benefits, such as suitability to past/current climate conditions and rapid economic returns. However, the longevity of these ECMs poses a risk of becoming outdated or ineffective as climate change alters the very climatic parameters they were designed for. This paper provides insight into the foresight required when selecting an ECM, focusing on its long-term viability under global warming. The ECMs discussed encompass passive strategies such as building envelope insulation and window design, active systems like efficient HVAC and heat pumps, and renewable systems. The need for context-specific ECM selection tailored to local climate, building type, and cost-effectiveness is highlighted. Major challenges and barriers influencing the widespread adoption of ECMs under global warming are addressed, including: 1) considering long-term effectiveness in ECM decision-making, as measures effective initially may become disadvantageous in the future, and vice versa; 2) adopting a life cycle perspective considering both embodied and operational impacts; 3) developing robust, resilient building designs under climate change uncertainty; and 4) potential strategy shifts, such as transitioning from passive cooling techniques to active cooling systems. Meanwhile, policy interventions through regular updates to building codes and standards are needed to keep pace with evolving climate conditions and engage diverse stakeholders to balance multiple objectives, including environmental, societal, and human factors. Incorporating climate change into decision-making for ECM implementation is paramount for building energy adaptation to a warming climate. [ABSTRACT FROM AUTHOR]

• A comprehensive review on building retrofit measures towards net-zero energy buildings is conducted. • Climate and occupant activities of fifty case studies are surveyed. • Passive, active, and renewable building energy retrofit measures are discussed. • The assessment methodology of retrofit measure selection is detailed. • Future studies are proposed to fully explore the potential of net zero energy buildings as sustainable structures. High energy consumption is a critical problem that governments are trying to overcome. The concept of Net Zero Energy Building (NZEB) has recently emerged as one of the solutions that can help address this problem, reduce pollution impacts, and mitigate the effects of global warming. The present paper discusses the current research on building energy retrofitting through a comprehensive review of more than fifty case studies, highlighting the ones that aimed to reach a NZEB. The passive, active, and renewable retrofit measures used are discussed. The assessment methodology for choosing the retrofit measures are classified into multi-criteria and optimization approaches and the available simulation tools and algorithms are detailed. The review also indicates that photovoltaic panels are used in retrofitting in all case studies to achieve NZEB, with the tilt angles of photovoltaic panels and solar collectors steeper in colder climates. It is noted that retrofitted residential NZEB buildings exhibit the lowest total energy consumption across all climate types when compared to other NZEB types, such as educational, business, and institutional buildings. Needed research directions include taking into account climate change in the modeling, and the use of bio-based materials to further reduce the building carbon footprint. [ABSTRACT FROM AUTHOR]

[Display omitted] • PCM is an effective energy flexibility asset by optimizing designs of PCM and heating operation. • Resetting temperature setpoints was applied to enhance the potential for cost savings. • PCM placement is more effective in interior walls than in interior roofs. • PCM impact on energy savings varies by seasons, negative in spring but positive in autumn. • Annual cost savings of 21.0% with Spot price is achieved. In cold climates, residential buildings are often well-insulated to reduce heating costs but may lead to overheating. Incorporating Phase Change Material (PCM) into building envelope can serve as an effective measure to mitigate overheating and reduce cooling costs. However, the inappropriate incorporation of PCM in well-insulated building envelope might negatively affect the heating energy flexibility. Therefore, this paper explores the effectiveness of PCM in reducing heating costs by optimizing the PCM design and heating operation. The main objectives are to (i) quantify the performance of PCM in terms of overheating and cost savings, aligned with the recently introduced Time-of-Use (ToU) tariff and Spot pricing, (ii) identify the optimal design of PCM-incorporated building envelope, and (iii) apply a smart control with resetting temperature setpoints in-line with EN-ISO 52120-1 Standard. A multi-stage optimization process has been designed. It first identifies the optimal properties (i.e., melting temperature), position, and thickness of PCM. Secondly, it optimizes the profile of temperature setpoints, considering the seasonal thermal behavior of PCM. Co-simulation platform between IDA ICE 5.0 and Python 3.8 optimization engine is created for the heating control optimization. Results showed that PCM is more effective when incorporated into the interiors of external walls rather than in roofs. The optimal PCM design for Nordic buildings, featuring 75 mm-thick PCM with a melting temperature of 21 °C positioned within walls' interior, has a varied impact on monthly energy savings across seasons, with a negative effect in spring and a significant positive effect in autumn. After the multi-stage optimization, the optimal scenario enables to achieve a maximum annual cost savings of 6.5 % with ToU tariff and 21.0 % with Spot pricing, while also eliminating overheating. [ABSTRACT FROM AUTHOR]

Ductless splits can have several advantages over conventional systems including energy saving, improved thermal comfort, and performance. A deep understanding of the effect of changes in operating conditions of the unit is required to estimate the energy saving in the field. In this paper, a reference guide is created to classify the effects of different parameters on the performance of the ductless split unit. The experiments are categorized into three main topics: 1- The effect of setpoint temperature on the performance and energy consumption, 2- The effect of evaporator fouling on energy consumption, and 3- The effect of condenser fouling on energy consumption of a ductless split unit. Results show that one degree Celsius increment in room setpoint temperature reduces power consumption approximately 57 W and in annually energy consumption about 279 kWh. Furthermore, covering 30 % of the evaporator coil surface area and comparing with the normal case (evaporator coil with no coverage), parameters of Energy Efficiency Ratio, energy consumption, and cooling capacity drops 13.5 %, 6.4 % and 19.1 %, respectively. Likewise, by covering 1/3 of the condenser coil surface area and comparing with the normal case, Energy Efficiency Ratio, and cooling capacity drops 9.8 % and 2.4 %, respectively. The mentioned data indicate that the effect of the evaporator dirtiness on reducing the cooling load of the device is much higher than that of condenser. [ABSTRACT FROM AUTHOR]

• Indoor air quality and mechanical ventilation in a school building. • Comparison of mechanical ventilation control strategies. • Integrating detached mechanical ventilation systems in existing school buildings. • Ventilation control strategies in simulations and monitoring. • Threshold-driven ventilation VS scheduled ventilation. Demand-controlled ventilation systems and carbon dioxide monitoring are critical to ensure indoor air quality (IAQ) comfort conditions. Their importance has become significantly more evident since the COVID-19 pandemic crisis. Control strategies for mechanical ventilation based on CO 2 concentration are commonly used, although they are rarely applied in Italian and other Southern European country schools. Moreover, optimised ventilation systems can help maintain indoor air quality while reducing energy consumption for heating (i.e. exploiting heat recovery). The proposed paper aims to compare different remote-control strategies to manage three detached mechanical ventilation units (DMV) installed in a junior-high-school demo building in Torre Pellice (Northwest Italy), monitored with room detail since April 2021. Eight IAQ control logics are analysed considering both in situ tests, handling DMV accordingly to room sensors, and simulation tests using the Energy Management System of EnergyPlus in the neutral season. All different solutions are compared by considering air quality, temperature, and energy need indicators. Additionally, a yearly energy verification via hourly simplified analyses is performed by considering fan consumption and energy conservation. Threshold-based control logics result to be more effective than time-dependent ones, while all cases guarantee very high IAQ levels, although energy savings are limited. The choice of the correct control threshold(s) is site-dependent being influenced by user local habits. [ABSTRACT FROM AUTHOR]

[Display omitted] This article introduces a new method of combining closed and semi-open spaces in a mid-rise office building. This method presented in the paper aims to improve energy efficiency and thermal comfort and emphasize the use of open spaces on all floors and in different directions. Usually, open spaces in buildings such as courtyards, balconies, atriums, and sky_court, despite their positive performance, have limitations in terms of geometry, location, enclosure, and connection with the building. However, this approach has tried to solve these limitations using a modular network in the plan production process. This approach allows the creation of open spaces with various geometries and areas on the floors. This study pays attention to the presence of semi-open space in mid-rise buildings and the creation of porosity in the volume, which reduces energy consumption and increases the comfort of employees. This article introduces an innovative approach to utilizing semi-open space on the floors of office buildings. This approach, by allocating a part of the area of each floor to semi-open space with two different parameters, i.e., "semi-open space with the most connection with the façade" and "semi-open space with the least connection with the facade," is a framework for the design and optimal placement of the semi-open space in It offers office buildings. This research tries to generate all possible situations to achieve 40 % of open space in the building. By dividing the plan into 5 × 5 modules in the presented simulation model, 320 modules are available, and to convert 40 % of the building into semi-open space, 128 modules should be allocated for this purpose. The models simulated by Ladybug and Honeybee plugins show that in the volumes where the semi-open spaces in the building have a combination of the maximum connection with the facade and minimum connection with the facade, they perform better in energy consumption and thermal comfort. The research results show that a volume comprising closed and semi-open spaces with a suitable porosity ratio can perform best. In other words, the change in volume porosity significantly affects energy consumption and thermal comfort. [ABSTRACT FROM AUTHOR]

This paper proposes a framework for multi-objective optimization of indoor daylighting and thermal comfort in natatoriums with retractable roofs, using genetic algorithms and building performance simulation. The goal is to balance daylight illumination and thermal comfort under different roof opening states. Efficiency of computational fluid dynamics simulation is improved by a multi-objective optimization framework based on non-dominated sorting genetic algorithms, integrating a generative adversarial network to learn the environmental map. The study analyses the impact of different roof opening ratios on environmental objectives and establishes regression equations to support optimization and accurate decision-making. In the optimal opening state, compared to a fully closed roof, the daylight factor and natural airflow velocity increase by up to 3.9 and 5.3 times, respectively, while the universal thermal climate index decreases by 1.0 °C. The introduction of a generative adversarial network proxy model significantly improves computational efficiency, accelerating natural airflow velocity calculation by 70–100 times and reducing simulation time by 98.6 %. This study expands predictive models from the urban scale to the building scale, enhancing simulation efficiency and promoting the application of deep learning models and genetic algorithms in climate-responsive building environment assessment and prediction. [ABSTRACT FROM AUTHOR]

[Display omitted] • The experimental system of heat load data acquisition for building air conditioning is constructed to obtain real and reliable data. • The Latin hypercube sampling is introduced to improve the initialization population of the sparrow search algorithm, which improves the performance of the algorithm. • A total of 12 models were implemented to forecast the heat load of building air conditioning, and ISSA-LSTM model shows the highest forecasting accuracy. • Deep learning and machine learning models were developed to forecast the heat load of building air conditioning and the differences between different models in forecasting the heat load of air conditioning have been identified. • The accuracy differences of heat load forecasting by optimizing LSTM model with different population algorithms have been studied. Building air conditioning heat load is a critical demand response resource, and its forecasting serves as a fundamental basis for optimizing building energy consumption control. The existing air conditioning load forecasting model suffers from reduced accuracy and stability due to the significant influence of temperature, humidity, and other factors. This paper proposes a method for building air conditioning heat load forecasting using Long Short-Term Memory neural network (LSTM) optimized by the Improved Sparrow Search Algorithm (ISSA). First, an experimental system for data collection on building air conditioning thermal load is constructed to form a dataset. Subsequently, the Latin Hypercube Sampling (LHS) is introduced to improve the SSA and iteratively optimize the hyperparameters of the LSTM model using ISSA. Finally, different optimization algorithms including Particle Swarm Optimizer (PSO), Crow Search Algorithm (CSA) and Spider Wasp Optimizer (SWO) were developed to optimize the LSTM model to achieve similar purposes, using the coefficient of determination as an indicator for evaluating model accuracy. The results show that the proposed data-driven new method is the most accurate model for forecasting building air conditioning heat load in this study. The R2 of the ISSA-LSTM forecasting model is as high as 0.9971, and compared with the RNN, LSTM, GRU, Bi-LSTM, SWO-LSTM, and SSA-LSTM models, the RMSE of this model in air conditioning heat load forecasting is reduced by 80.9670%, 71.7390%, 87.8040%, 88.3027%, 86.1179%, and 47.3089%, respectively. Employing the ISSA-LSTM method significantly enhances precision in building air conditioning load forecasting and holds promising applications in optimizing building energy consumption control. [ABSTRACT FROM AUTHOR]

• Analyze impact of refrigerant undercharge faults on HVAC and indoor conditions. • Investigate significant variations in supply air temperature and energy use. • Discuss challenges in detecting refrigerant undercharge faults. • Identify data points that help in detecting refrigerant undercharge faults. This study investigates the impact of refrigerant undercharge on indoor temperature and HVAC system performance in residential buildings. Simulation models for typical residential buildings in Orlando, FL and Indianapolis, IN were developed using the ResStock database. A refrigerant undercharge fault model was then applied to the simulations with varying levels of fault intensity. The paper offers an extensive analysis, revealing that variations in supply air temperature, equipment runtime, and cooling energy consumption due to the level of refrigerant undercharge faults are notably significant on a summer representative day. Similarly, on a winter representative day, changes in supply air temperature and runtime are significant as well as changes in supplemental heat energy consumption. We find that occupants may remain oblivious to these faults during the cooling season, particularly when the HVAC system is oversized; in that case, supply air temperature data could help detect a fault. Another challenge is that during the heating season, when the supplemental heater operates, it is difficult to identify a refrigerant undercharge fault using only indoor and supply air temperature data. This study finds that supply air temperature, equipment runtime, and supplemental heater energy consumption data can help in detecting refrigerant undercharge faults. [ABSTRACT FROM AUTHOR]

Precise forecasting of the cooling load (CL) of buildings is crucial for the efficient functioning of central air conditioning systems. The study proposed a new hybrid model based on empirical modal decomposition (EMD) and Markov chain improved LSTM neural network, denoted as EMD-LSTM-Markov. First, Pearson's correlation coefficient (PCC) and Gradient Boosting Decision Tree (GBDT) are used to extract features with a high degree as the input data. Then, the LSTM is employed to establish a prediction model. Finally, to verify the validity and prediction accuracy of the proposed model, this paper selects the CL data of an office building in Guangzhou as the research sample. The results show that the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) of the EMD-LSTM-Markov model are 4.53 and 0.84%. Compared with the other three predicting models, the RMSE and MAPE values of EMD-LSTM-Markov are decreased by 40% − 94% and 70% − 96%, which has better performance in accuracy and stability. It testifies that the proposed strategy is reliable and effective. This work successfully resolves the issue of delayed cold response. The hybrid model proposed demonstrates significant energy-saving potential for accurately predicting building CLs and enhancing the optimal control of cooling systems. Future research should explore the hybrid model's robustness across different building types and climatic regions. [ABSTRACT FROM AUTHOR]

A number of occupancy-sensing methods have been proposed to date with the goal of saving HVAC energy in commercial spaces by means of demand-controlled ventilation. Currently, CO 2 measurement is the most common approach deployed in practice. However, recently a number of other approaches have been proposed, such as using surveillance cameras, depth sensors, thermal imagers, etc. In this paper, we compare the occupancy-sensing performance of high-accuracy commercial CO 2 sensors and overhead fisheye cameras supported by deep-learning algorithms. Our experiments are conducted over 3 days in a large university classroom with highly-dynamic occupancy. First, we assess the impact of parameter selection and sensor placement on CO 2 -to-occupancy conversion accuracy. Then, for the best-performing setup, we compare this accuracy against that of fisheye cameras. Subsequently, we estimate the potential energy savings offered by both systems. Our results show that overhead fisheye cameras produce on average a 20% lower occupancy-estimation error than CO 2 sensors in steady-state periods, and almost 70% lower error in transient periods across 4 performance metrics used in this work. In terms of potential energy savings, fisheye cameras offer on average 6 percentage points of additional savings compared to CO 2 sensors. Given their relatively low cost and implementation simplicity, CO 2 sensors are an attractive approach to saving HVAC energy, however their occupancy-estimation accuracy is highly sensitive to parameter selection that is challenging in practice. Occupancy sensing based on fisheye cameras is not subject to similar parameterization sensitivity and in addition to outperforming CO 2 sensors allows precise localization of occupants in a space thus potentially enabling additional space-management and safety/security services, that CO 2 sensors cannot offer. [ABSTRACT FROM AUTHOR]

• A laboratory experiment on the energy pile in unsaturated soils is conducted. • A model considering the coupled heat and moisture transfer is established. • The impacts of thermal imbalance on long-term performance are discussed. • A complete case study is carried out for practical engineering. • The economic and technical feasibility is analyzed for design optimization. The operation of energy piles in unsaturated soils involves coupled heat and moisture transfer, demanding further detailed investigation. In this paper, laboratory tests were conducted to validate a numerical model that incorporated vapor and liquid migration, convection, and latent heat transfer in unsaturated soils. Moreover, this model captured the dynamic variation in characteristic parameters of unsaturated soils. Building upon this observation, this study delved into the thermal performance of the energy pile under ten-year cooling and heating cycles, specifically focusing on the temperature, heat exchange rate, volumetric water content, and thermal conductivity. It is demonstrated that the imbalanced cooling and heating load adversely affects the long-term operation. In particular, the cooling power experiences a reduction of 15.1% after ten years of operation. The results indicate that higher soil temperatures positively impact the heat exchange rate in heating mode, while rainfall facilitates heat transfer in both seasons. Furthermore, a case study of an energy pile system was carried out, encompassing load calculation, pile group simulation, economic analysis, and design optimization. A comprehensive evaluation of the technical and economic feasibility demonstrates that the system exhibits excellent performance, particularly when the ratio of cooling and heating loads ranges from 1.1 to 1.3. [ABSTRACT FROM AUTHOR]

• Novel flexible heat pump integrating heat storage with new defrosting mechanism. • Uninterrupted heating and constant capacity during the defrosting process. • Optimization of the storage temperature to maximize COP improvement. • Impact of defrosting time was investigated. • Up to 13.2% of COP improvement compared to a baseline heat pump with R1234yf. There has been growing interest in using air-source heat pumps as an alternative to potentially replace fossil-fuel-based heating technologies for heat decarbonisation. One of the technical issues that hinders the wide uptake of air source heat pumps is the decreasing energy efficiency caused by frequent defrosting operations under low ambient temperatures. Currently, the most widely used defrosting method is the reverse-cycle defrosting but this operation would have to interrupt the heating supply during the defrosting process. The authors recently proposed, developed, and demonstrated a flexible heat pump concept that recovers partial of the subcooled heat from the hot refrigerant exiting the condenser and stores it in an integrated thermal storage. The stored heat could later be used to save compressor power and defrosting applications. In this paper, we proposed an innovative method by using a multi-valve design of the flexible heat pump to achieve the defrosting function. In this new concept, defrosting is achieved by condensing the refrigerant inside the evaporator and simultaneously ensuring uninterrupted heating during the heat storage discharge process. A model has been established to study the heat pump performance during a full charge/discharge cycle of the storage. Results indicated that with R410a, R134a and R1234yf, the proposed flexible heat pump has the potential to respectively save up to 11.2%, 10.8% and 13.2% of compressor power, in comparison with a heat pump using the conventional reverse cycle defrosting method. Concurrently with the defrosting operation, the same heating capacity is ensured without interruption by the flexible heat pump. [ABSTRACT FROM AUTHOR]

A baseline model possessing both high accuracy and robust generalization capabilities is crucial for the operation evaluation and optimization of district heating (DH) substation. However, existing modeling approaches struggle to achieve a balance between accuracy and generalization. Specifically, physics-based models exhibit strong generalization but suffer from poor accuracy, while data-driven models demonstrate high accuracy but lack generalization. This paper, therefore, proposes a baseline model combining physics and data-driven approach to evaluate hourly supply temperature of DH substation. In this hybrid model, the physics-based approach is utilized for static feature learning, while the data-driven method handles dynamic feature learning of the supply temperature. Validity of this baseline model is confirmed by a practical case, which suggested that this model can effectively learned the static and dynamic features of supply temperature with a 3.49% error for daily value and 1.39% for hourly coefficient of daily profile. According to the generated baseline by this model, all hourly supply temperatures are labeled with evaluation results, with 30.8% being in normal operation state, 29.5% being too high, and 39.7% being lower than theoretical values. This baseline can also serve as an optimization target for unreasonable supply temperature settings. Furthermore, the effectiveness of the proposed baseline model is further demonstrated through a comparison with a common data-driven model. [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]

The UAE school sector impacts several Sustainable Development Goals of the UAE Vision 2021. The sector is designed according to a standardized system of Model buildings, repeated several times throughout the country, comprising facilities that are often obsolete at both functional and technological level; hence, frequently presenting poor energy performances. The paper presents the first comprehensive review of the energy consumption state-of-the-art of the public-school sector in the Emirate of Sharjah, United Arab Emirates, including the first necessary dataset for future upgrade actions. The study was completed by means of a deep mapping, functional, typological, and pedagogical analysis. The collected data of real energy consumption of 69 schools highlighted an average energy consumption of 174 kWh/m2 year, with very consistent results for each educational cycle, even if a larger variance was expected due to difference in size, typology, and working hour. More significant differences can be observed when comparing the result of school with different typologies, stressing the relevance of further investigating the standardized Model system. To quantify their correlation to the buildings' energy consumption, linear regression analyses were conducted on a number of notable factors, such as building age, net floor area, roof area, shape coefficient, number of students, number of classrooms and labs, presence of gyms and multipurpose halls, and implementation of digital technologies. Schools' capacity/occupancy ratio, net floor area and roof area, and the use of energy-demanding devices for novel smart learning techniques were found having the highest impact. The tested multiple regression model, which displays a marginal error of 5–30 % when applied to single buildings and 9 % when used to assess larger clusters, could facilitate the estimation of the energy consumption of schools and support stakeholders' decision making. The outcomes of the study can assist governmental agencies in elaborating suitable refurbishment policies for the energy and functional upgrade of the educational sector, both at the building and urban scale, and school administrator in tackling appropriate actions toward the schools use and resources optimization. [ABSTRACT FROM AUTHOR]

[Display omitted] • The heat exchange performance of an energy pile under different intermittent field operating conditions is compared with that of a borehole heat exchanger. • The thermal impact on the pile concrete and the surrounding soil of the energy pile is characterized. • The differences in heat exchange rates between continuous and intermittent operating conditions for the energy pile are quantified. • The impact of intermittent operating modes of the energy pile on heat transfer efficiency and energy consumption is explored. This paper examines the difference in heat exchange efficiency between an energy pile and a borehole heat exchanger under intermittent operation conditions through a field underground energy structure located in Liyang City, Jiangsu Province, China. Additionally, a 3D finite element numerical model was established to simulate and explore the effect of the intermittent ratio on the heat exchange efficiency of the energy pile under intermittent conditions. This study shows that under intermittent constant power heating, the heat exchange rate per unit pipe length of the energy pile was much higher than that of the borehole heat exchanger. Compared to continuous operation, the average temperature change of the pile body caused by intermittent operation decreased by 59.30%, and that of the soil decreased by 72.90%. Moreover, intermittent operation has the dual advantage of improving heat exchange efficiency and marginally increasing the Coefficient of Performance of the energy pile system, thereby resulting in reduced electrical consumption. By providing field data about the thermal performance of the energy pile and the borehole heat exchanger under intermittent operating conditions, our results contribute to solving the problem of thermal imbalance caused by the long-term continuous operation of energy piles, thereby optimizing the design and operation of heat exchangers and promoting more efficient and sustainable use of geothermal energy. [ABSTRACT FROM AUTHOR]

[Display omitted] • Performance of medium-deep geothermal heat exchanger was studied. • Logging data and tested heat exchanger performance were presented. • Algorithm was validated with less than 10.65% relative error after stabilization. • Heat exchange rate varies from 169 kW to 517 kW under different operating parameters. • Lifecycle carbon emission changes from 18.5 kg·kW−1 to 9048.8 kg·kW−1. Geothermal energy is one of the most competitive renewable energies and the medium-deep geothermal energy has been broadly adopted for building heating. This paper reported a long-term performance study on the medium-deep ground heat exchangers (GHEs) based on a project in Xixian New Area, China. The logging data of a 2,500 m well and the tested inlet/outlet temperature were presented, before a numerical simulation algorithm was validated and adopted to predict the GHE long-term performance affected by operating parameters. It turns out that the algorithm can forecast the performance with a maximum relative error of less than 10.65 % at the stabilization stage. Under long-term operation, the average heat exchange rate becomes stable since the eighth year. The heat exchange rate varies from 169 kW to 517 kW through the life cycle when changing flow rate and daily operating time. The carbon emission needed for per unit heat extraction ranges from 18.5 kg·kW−1 to 9,048.8 kg·kW−1. Properly adjusting the operating parameters according to the building load will be beneficial to reducing carbon emissions and improving heat extraction efficiency. It is expected that the results will be conducive to the high-efficiency and low-carbon usage of geothermal energy. [ABSTRACT FROM AUTHOR]

Advancements in data center high performance cooling. [Display omitted] • The development of predominant air supply method in data centers was analyzed. • The supply/return water temperatures and large temperature difference technologies were discussed. • The components and configurations of data center cooling systems were studied. • Different intelligent management strategies of cooling systems were compared. To expedite the achievement of energy conservation and consumption reduction in data centers, it is essential to not only utilize ambient energy effectively and maximally but also to enhance the energy efficiency of mechanical refrigeration. The synergy between free cooling and mechanical refrigeration plays a crucial role in reducing the power usage effectiveness (PUE) of data centers. This, in turn, maximizes the energy efficiency of the cooling systems, ensuring their safe, stable, and efficient operation. This paper synthesizes research on various aspects of cooling systems for data centers, including air distribution, supply and return water temperature, system components and configurations, and intelligent management. The analysis provides a vital reference for the development of environmentally friendly and efficient cooling systems in data centers. [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]

To ensure smooth discharge of heat from air conditioning condensers in subway trains, it is important to keep the air temperature within the prescribed limit in the tunnel. Additionally, maintaining a suitable CO 2 concentration is essential for preserving air quality inside the carriages. In China, heat removal systems are commonly installed in subway tunnels to achieve these objectives, although the actual effectiveness has not been extensively examined. This paper focuses on a typical subway station in Tianjin. The effects of this system on tunnel temperature and CO 2 concentration were studied through field measurements and numerical simulations. The results indicate that the heat removal system introduces hot air from outside during summer leading to an increase in tunnel temperature, while the excluded air temperature is the average temperature in the tunnel. Since Tianjin is located in cold regions, the tunnel wall temperature decreases in winter, so the tunnel air can be cooled by tunnel walls in summer. The heat removal system has little impacts on the fresh air change rates, which are relatively 2.22 and 1.55 times per hour when the system is turned on or not. However, the maximum temperature and CO 2 concentration in the interval tunnel are 28.5 °C and 550 ppm, which are lower than the limitation of 40 °C and 1500 ppm even without the heat removal system. In conclusion, the installation of heat removal systems in subway tunnels located in cold regions is unnecessary. This can help reduce investments, save space, and minimize energy consumption. [ABSTRACT FROM AUTHOR]

• An easily implemented, measurement-based calculation procedure to determine the SHGC of BIPV glazing for product comparison is proposed. • The component-based SHGC procedure for BIPV glazing takes optical inhomogeneity and the extraction of electricity into account. • The adopted area-weighting approach allows the SHGC to be determined for a large variety of dimensions and cell coverage ratios in BIPV glazing from a small set of input data. • The absolute reduction in SHGC due to extraction of electricity is greater for single PV laminates providing relatively little thermal insulation than multiple-pane BIPV glazing units. • SHGC values for BIPV glazing products depend sensitively on the choice of boundary conditions assumed. Building-integrated photovoltaic (BIPV) systems are intrinsically designed to generate electricity and to provide at least one building-related function. When BIPV modules act as glazing products in windows, skylights or curtain walls, their ability to control the transmission of solar energy into the building must be characterised by a Solar Heat Gain Coefficient (SHGC) or g value (also known as Total Solar Energy Transmittance – TSET – or "solar factor"). For the comparison of BIPV glazing products consisting of one PV laminate and possibly further, conventional glazing layers separated by gas-filled cavities, the procedures documented in international standards for architectural glazing (e.g. ISO 9050 and EN 410) form a suitable starting point. Easily implemented modifications to these procedures are proposed to take both optical inhomogeneity (if relevant) and extraction of electricity from BIPV glazing units into account. Geometrically complex glazing and shading devices, and light-scattering glazing layers, are outside the scope of the proposed methodology; SHGC determination for obliquely incident solar radiation is also excluded. For these cases, the experimental calorimetric approach documented in [ISO 19467:2017; ISO 19467-2:2021] is recommended. The paper also presents results and conclusions from an implementation exercise and sensitivity study carried out by participants of the IEA-PVPS Task 15 on BIPV. The cell coverage ratio in the PV laminate, the thermal resistance offered by the glazing configuration, the choice of boundary conditions and the effect of extracting electricity were all identified as parameters which significantly affect the SHGC value determined for a given type of BIPV glazing. A practicable approach to accommodate the great variety of dimensions typical for BIPV glazing is also proposed. These findings should pave the way for modifying the existing component-based standards for architectural glazing to take the specific features of BIPV glazing into account. [ABSTRACT FROM AUTHOR]

Smart building techniques have drawn considerable attention for the potential to accommodate renewable energy resources and achieve local energy sustainability. However, information privacy, growing number of users and vulnerability of communication channels have led to energy management and cyber-security issues. In this paper, a cyber-attack resilient energy management system for smart residential buildings is proposed. An aggregative game is formulated to describe the non-cooperative interactions among smart buildings that are coupled through dynamic electricity pricing. The existence of the variational equilibrium is analyzed theoretically. Moreover, a semi-decentralized algorithm is employed to attain the equilibrium solution without disclosing private information of individual buildings. To detect potential price attacks, a cyber-attack detection mechanism is proposed by comparing the predicted and actual electricity costs and exchanged power based on the long short-term memory network model. Numerical simulations demonstrate the performance of the semi-decentralized algorithm in term of convergence speed and robustness against disturbances. Additionally, the effectiveness of the resilient energy management strategy is investigated. [ABSTRACT FROM AUTHOR]

• Private organizations focus on high-quality renovations and innovative financing. • An integrated service approach involving multiple stakeholders is crucial. • Innovative financial solutions are crucial for the success of energy renovations. • Barriers for Homeowner associations are linked to the renovation process. • Strategies of Integrated home renovation service providers were analyzed. Across the European Union (EU), approximately 46% of the population resides in apartment buildings. A majority of these buildings are in need of energy renovation and are managed by Homeowner associations (HOAs). HOAs face distinct challenges in renovating their buildings. Integrated home renovation services (IHRS) have emerged as a potential solution to accelerate highly energy-efficient renovations for HOAs. The question arises: How do current integrated home renovation service providers facilitate energy renovations for homeowner associations, overcoming the prevailing barriers to uptake? This paper explores the current state of IHRS providers operating in four European regions with a focus on the Netherlands and Flanders. The study offers insights into the dynamics of their business models, strategies, and ways forward. The methods employed involve a combination of qualitative approaches. Fourteen business models of IHRS providers in the Netherlands, Belgium, France, and Austria were analysed, to define their strategies within the regional context. The results reveal that while public entities prioritize broad societal challenges, private models focus on operational efficiency and financial sustainability. Public-private partnerships offer a hybrid approach, blending strengths but requiring precise governance to align diverse interests and achieve comprehensive, sustainable outcomes. The findings can inform policymakers, practitioners, and researchers in developing effective and targeted IHRS initiatives in the EU. [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]

• Criteria for assessing the applicability of air-side economizers were proposed, encompassing energy-saving potential and corrosion risk. • The consistency between available hours with different counting methods and energy-saving potential was compared. • When adopting isenthalpic humidification, humidification energy consumption accounts for less than 6% of the cooling energy consumption, so not considering the lower limit of DPT has little impact on the assessment of the energy-saving potential. • More reasonable indicators and indicator values were screened for applicability assessment and climate zoning. The application effect of air-side economizer is closely related to outdoor air temperature, humidity and cleanliness, leading to significant variations in different regions. In this paper, the key factors affecting the applicability of air-side economizers were analyzed in terms of energy-saving potential and corrosion risk, and their assessment criteria were screened out. Then the K-mean cluster analysis was performed on zoning indicators from assessment criteria, and two zoning results were obtained, both of which could divide the country into eight climate zones. To determine a more reasonable zoning result, the consistency between available hours with different counting methods and energy-saving potential, as well as the effect of humidification energy consumption on energy-saving potential was discussed through energy consumption analysis. We conclude that the humidification method has a significant impact on the energy consumption and zoning results of the air-side economizer. When adopting isenthalpic humidification, it is more appropriate to assess the energy-saving potential of the air-side economizer by the annual available hours with DBT out ≤ 27 °C and DPT out ≤ 15 °C. Additionally, the zoning result obtained with this and pollutant concentration indicators is more reasonable. In the zoning results, Zone I is most suitable for applying air-side economizers due to its large energy-saving potential and low corrosion risk, but only includes Lhasa. However, Zone VII and Zone VIII are deemed to have high corrosion risk, resulting in higher costs for risk control in these areas, which comprise Taiyuan, Urumqi, Xi'an, Zhengzhou, Tianjin, Jinan and Shijiazhuang. [ABSTRACT FROM AUTHOR]

• We develop an accurate hybrid modeling approach for building infiltration. • It combines machine learning categorization and grey-box sub-modeling. • Accuracy and generalization of grey-box infiltration models are improved. • A case study under a standard test environment demonstrates its effectiveness. Infiltration is responsible for one-third to one-half of the space conditioning load of a typical residential home, but the modeling of infiltration for building energy modeling is either represented by over-simplified equations or dependent on over-generalized rules of thumb. This paper develops a physics-informed data-driven methodology for modeling infiltration using building-specific empirical measurements. The developed hybrid methodology combines machine-learning categorization and grey-box sub-modeling to improve the accuracy and generalization of commonly used grey-box infiltration models. The developed methodology excels at predicting infiltration by improving the ability to predict infiltration under unseen environmental conditions using machine learning algorithms with physical significance. In a case study conducted using the iUnit, a modular studio apartment experimental test facility located at the National Renewable Energy Laboratory, we use empirical airtightness measurements to fit an infiltration model using the developed methodology. We find that the developed methodology can improve the overall model accuracy by 43% and improve extrapolation by 38%, compared with the model based on the common grey-box infiltration equation. We also notice that the selected features can improve the performance of a pure machine-learning model, indicating that our methodology identifies the features with the most physical significance to infiltration modeling. [ABSTRACT FROM AUTHOR]

Energy disaggregation, or Non-Intrusive Load Monitoring (NILM), is a technique that predicts the consumption levels of individual appliances from only the main signal in the building. Various methods have been proposed to solve this problem, including sparse coding (SC), which offers great advantages due to its ability to capture complex patterns in data. However, a challenging aspect of NILM is that data containing appliance-level information is scarce. Moreover, the houses that the models are tested on might be from a different population than the training data, thus resulting in a domain shift. Therefore, we need to develop approaches that are adapted to training data scarcity through the use of transfer learning (TL). In this paper, we test several TL approaches on SC models with the aim of discriminative energy disaggregation (DD). We propose four TL SC models: "Transfer SC+DD", "SC source, DD target", "Transfer Deep SC" and "Transfer Variational SC". We compare these models with 4 models that do not apply domain adaptation. We show that TL-based models achieve similar and sometimes better results than the regular models. We also show that further training on only the aggregate signal of some target domain houses improves the performance of a SC model trained only on the source. [ABSTRACT FROM AUTHOR]

Fibrous materials, such as cellulose papers, fibres, or textiles, are regarded as potential evaporation materials used in evaporative cooling systems. The fibrous material's wettability and evaporation characteristic have significant influences on heat and mass transfer performance of evaporative cooling systems. This paper studied the wicking performance of selected fibrous materials including cellulose/PET fibre, Coolmax fabric and wood pulp paper by numerical modelling and dedicated experiment based on transient weight and optical measurements. The effect of evaporation on the performance of wicking rise were analysed in depth using the obtained experimental data. The measured data show good agreements with the predicted results, within ± 6.67%, 7.47% and 3.78% errors for the three types of materials, respectively. The microstructure parameters that control water diffusion performance of porous materials, i.e., permeability to effective pore radius ratio, were found to be highest for cellulose/PET fibre followed by Coolmax. Additionally, the study compared water evaporation rate of the selected materials by a few validated preliminary tests operating under various ambient air velocities and surface temperatures. Coolmax and cellulose/PET fibres were found to have similar higher evaporation rate than wood pulp paper. This paper presented numerical and experimental approaches for accurately characterising and differentiating wicking and evaporation performance of fibrous materials which would contribute to optimise evaporation materials for evaporative cooling systems. [ABSTRACT FROM AUTHOR]

Occupant behaviour on air conditioning directly influences building energy consumption. Current building energy simulation typically assumes that occupants operate air conditioners ideally ignoring the stochastic characteristic of occupant behaviour, which leads to a large deviation of predicted energy performance from real consumption. This paper proposed a methodology framework for modelling occupants’ stochastic behaviour on air conditioning. This method combined measured data, statistical analysis and logistic regression to derive stochastic behaviour models that give the probability of when and above which temperature occupants may turn on air conditioning, at what temperature they may set and when they may turn off air conditioning. The behaviour models developed in this paper were further incorporated in widely used building simulation engine—EnergyPlus. The accuracy of the developed simulation model was validated against field measurement. Using this model, the cooling energy performance was simulated and compared with results from building energy standard based settings. The result shows that simulation using settings according to building energy standard over-predicts cooling load and total cooling energy requirements by 113% and 5.6 times respectively, which are very large discrepancies and may lead to a misunderstanding of energy savings potential of energy efficiency measures. The methodology developed in this paper has generality and can be extended and applied in other buildings for constructing behaviour models of air conditioning. The limitations of the current research as well as future research works were also discussed. [ABSTRACT FROM AUTHOR]

Solar-based radiant heating systems represent a sustainable, and relatively low-cost, technology to raise the temperature of the interior thermal mass of our buildings. Through the use of direct absorption solar thermal collectors, the same working fluid which absorbs the solar energy can be used to transfer the energy for storage in the thermal mass of the structure using a network of pipes embedded in concrete floors. This study investigates a promising working fluid which can be used in such systems – one which is based on multi-walled carbon-nanotubes suspended in normal base fluids. A major stumbling block affecting the wide spread use of carbon-nanotube nanofluids is their low dispersion stability at elevated temperatures, which significantly reduces the absorption capabilities of the nanofluids and could lead to clogging of the pumps used to circulate the fluids. In this paper, we report on a scalable UV-ozone (UVO) treatment technique to produce highly stable dispersions for the elevated temperatures experienced by working fluids in radiant heating systems. To probe suitability of UVO treated multi-walled carbon-nanotube (MWCNTs) for solar-assisted radiant heating systems, this paper investigates the effects of exposure time and temperature on stability, optical absorbance properties, the extent of functionalisation, and the photothermal conversion performance of UVO-treated MWCNT nanofluids. No agglomeration or degradation of the MWCNTs was observed at elevated temperatures (up to 150   °C), highlighting the stability of proposed nanofluids. [ABSTRACT FROM AUTHOR]

Dynamic cooling and heating load forecasting of heating, ventilation and air conditioning (HVAC) systems is a basis for optimizing the operation of HVAC systems and can contribute to achieving the effective management for the HVAC systems. This paper proposes a load forecasting method for office buildings based on artificial intelligence and regression analysis, including wavelet transform, support vector machines (SVM), and partial least squares regression (PLS). An office building located in Tianjin, China is taken as the building case study to validate the proposed model. For selecting the input variables, the methods of sensitivity analysis and correlation analysis are used. The results of different prediction horizons, mainly including 1 h ahead, 2 h ahead, 3 h ahead and 24 h ahead forecasting, are finally provided. In order to illustrate the accuracy improvement of the proposed model, the other three models are built to compare with the proposed model. Further, the influence of weather forecast precision on the proposed model is analyzed in this paper. The results indicate that the proposed method can realize dynamic load forecasting with high accuracy for different time horizons. [ABSTRACT FROM AUTHOR]

The use of Phase Change Materials (PCM) in different building applications is a hot topic in today's R&D activities. Numerical simulations of PCM-based components are often used both for research activities and as a design tool, although present-day codes for building performance simulation (BPS) present some shortcomings that limit their reliability. One of these limitations is the limited possibility of replicating the effects given by thermal hysteresis – a characteristic of several PCMs. In this paper, an original algorithm that allows hysteresis effects to be accounted for is described and the results from simulations are compared against experimental data. The algorithm is implemented in EnergyPlus™ and makes use of the Energy Management System (EMS) group, one of the high-level control methods available in EnergyPlus™. The algorithm enables the replication of PCM's different heating/cooling enthalpy curves in this BPS tool, which just recently was equipped with an integrated module for the replication of the effects of thermal hysteresis. A comparison between numerical results from the proposed model and from other methods implemented in BPS tools, and the experimental data provided, shows the impact of the algorithm in the simulation of heat transfer in a PCM layer intent for opaque walls. In general, it is shown that the proposed method presents a better agreement with experimental data than alternative modelling approaches, but it is also seen that all the tested numerical models are not fully able to replicating the behaviour of PCM layers if the PCM does not melt or resolidify completely (i.e. it remains in the phase change) during the charge/discharge cycle. A local sensitivity analysis complements this study and highlights the most relevant parameters that influence the results of a simulation carried out with the proposed model. Finally, the paper hypothesises that the general discrepancy between simulations and experimental data seen in the case of incomplete melting of the PCM layer can be due to unsuitable data for the thermophysical behaviour of the PCM, which are obtained through conventional characterisation procedures far from the physics of the PCM layer when in real building structures. [ABSTRACT FROM AUTHOR]

With ever increasing energy generation diversity and energy storage becoming affordable, distribution networks are becoming more complex than ever before. This complexity can be utilized to benefit the distribution networks as well as end consumers in the form of controlled Microgrids. Microgrids are not simply distributed generation and energy storage systems; solar systems and battery banks, rather a complete design of hardware and software for specific uses and users. All the different elements need to be designed to work together to provide stable, efficient and sustainable power delivery to the end user. An experimental Hybrid-Microgrid testing facility is presented in this paper that implements high-efficiency distribution architectures combining both AC and DC networks. This testing facility provides a research testbed for investigating different aspects of Microgrid systems, employing a total of 15.5 kW of reconfigurable Solar PV and 80 kWh of lithium energy storage on a 145 kVA commercial building load located at Griffith University. Implementation results along with control system simulation results are presented in this paper for distributed renewable generation, Static Synchronous Compensators, advanced control methodologies and forecasting methods for energy management purposes. [ABSTRACT FROM AUTHOR]

Green roofs can decrease the energy consumption and improve air quality and durability associated with a roofing material. Previous studies have conducted simulations of the energy performance of green roofs over a limited period, and important parameters such as leaf area index (LAI) was set at a constant value. To develop an energy performance model for a green roof with a variable LAI which is suitable for annual simulation, this paper proposes a method to integrate a seasonally variable LAI equation into a green roof model based on an improved Dickinson's equation. After verification with measured data, this equation has a more accurate temperature setting for calculating seasonally variable LAI, the average error and average relative error are improved from 0.34 to 0.09 and 39.3 to 7.1%, respectively. Thus, it can be applied in different climatic regions. To determine the differences of annual energy performance of green roof, this paper compares simulation results of constant and variable LAI values for a two-story building in Shanghai, China. The simulation is conducted on the EnergyPlus platform. The results show that the simulations of temperature, heat flux, and heat load of the building for green roof modeling differ considerably for a constant and a variable LAI. In summer, the calculated latent heat flux is usually underestimated and the sensible heat flux is usually overestimated. Moreover, if the selected LAI value is higher than that of actual data in winter, the calculated latent heat flux is overestimated and the sensible heat flux is underestimated. The simulation results are more relevant for a seasonally variable LAI. [ABSTRACT FROM AUTHOR]

Hospitals present a widely recognized margin for energy conservation due to their elevated final energy use intensity. Specifically, operating rooms are among the functional areas with greatest energy demand in hospitals, given the stringent indoor conditions for patients and surgical staff. However, energy efficiency in surgical facilities has traditionally been overlooked by the scientific literature in favor of guaranteeing adequate air quality conditions. This paper assesses the potential energy savings that could be achieved in a surgical suite in a Spanish hospital by lowering the current ventilation rates to the lowest energy-demanding values recommended by the proper Spanish standard and other international codes. Likewise, the paper assesses the actual hygrothermal and ventilation conditions in the suite based on the results of a continuous monitoring campaign performed for a period of 12 days. Simulations undertaken with a dynamic energy model specifically developed and calibrated for this work revealed that the average energy intensity of the suite is 1021 kWh/m 2 /year (1685 kWh/m 2 /year for operating rooms), with 80% of the total annual thermal demand corresponding to periods of inactivity. In addition, energy savings between 40% and 80% are possible while complying with both national and international standards. [ABSTRACT FROM AUTHOR]

This paper provides practical guidelines to the Bayesian calibration of building energy models using the probabilistic programming language Stan. While previous studies showed the applicability of the calibration method to building simulation, its practicality is still impeded by its complexity and the need to specify a whole range of information due to its Bayesian nature. We ease the reader into the practical application of Bayesian calibration to building energy models by providing the corresponding code and user guidelines with this paper. Using a case study, we demonstrate the application of Kennedy and O’Hagan’s (KOH) [1] Bayesian calibration framework to an EnergyPlus whole building energy model. The case study is used to analyze the sensitivity of the posterior distributions to the number of calibration parameters. The study also looks into the influence of prior specification on the resulting (1) posterior distributions; (2) calibrated predictions; and (3) model inadequacy that is revealed by a discrepancy between the observed data and the model predictions. Results from the case study suggest that over-parameterization can result in a significant loss of posterior precision. Additionally, using strong prior information for the calibration parameters may dominate any influence from the data leading to poor posterior inference of the calibration parameters. Lastly, this study shows that it may be misleading to assume that the posteriors of the calibration parameters are representative of their true values and their associated uncertainty simply because the calibrated predictions matches the measured output well. [ABSTRACT FROM AUTHOR]

New smart building technologies that offer continuous dynamic optimization of Heating, Ventilation, and Air Conditioning (HVAC) control hold promise to advance building operations for efficiency and grid response. These technologies use data from the control system to determine the analytically optimal setpoints, and then write back the optimal setpoints into the control system to minimize system energy consumption or costs. There are limited studies documenting field validations of these technologies. This paper presents the results from a long-term field evaluation of a model-predictive HVAC optimization system that installed in four commercial buildings. Energy savings analysis was conducted based on pre/post submetered energy use. Across the cohort of evaluation sites, HVAC savings following the implementation of the optimization system were mixed, ranging from 0–9%. Analysis of site operational data showed that occupant comfort was neither positively nor negatively impacted. Key technology adoption considerations and recommendations are summarized in the paper. The technology performs best when HVAC systems are in good working condition, and can be exercised to achieve the full range of its optimized setpoints–however it may not provide extensive additional savings over cases where best practice sequences of operation and reset strategies are already comprehensively implemented. [ABSTRACT FROM AUTHOR]

The implications of optimised structural designs in the life cycle carbon performance of buildings have been systematically overlooked in previous studies. The paper addresses this common limitation offering an integrated sustainable structural analysis at building level. To achieve this, a BIM-embedded approach was established utilising embodied carbon metrics and results from heuristic structural optimisation. A real building scenario was used to test the proposed approach in the context of multi-storey reinforced concrete (RC) buildings. Results show how the building life cycle performance is affected by the use of structurally optimised designs. The structural floors in particular, not only cover the largest proportion of the embodied carbon in the structure but they are also responsible for a large proportion of the carbon emissions of the tested building elements. The results obtained in this paper justified the need for more comprehensive efforts in the design optimisation of RC floors. Overall, it is suggested that the unclear interpretation of the optimisation outputs would result in the selection of structural designs that could compromise the buildings carbon performance. [ABSTRACT FROM AUTHOR]

This paper provides a review of the literature concerning the impact of temporarily increased cooling setpoint temperature on occupant thermal comfort during demand response (DR) events in commercial, air-conditioned buildings. We address concerns regarding thermal comfort as it relates to zone temperature modifications that may be implemented as a part of DR measures. Increased zone setpoint temperatures during the cooling season can adversely affect building occupants physically and psychologically, and impair their perceived indoor air quality and self-estimated performance. In some cases, however, improved occupant thermal comfort due to warmer zone setpoint temperatures during DR events has also been reported. In order to reduce the negative impacts on building occupants, DR must be implemented with careful control and monitoring. There are significant differences in assumptions made about the way people would respond to a thermal environment between the static heat balance model and the adaptive approach, with the adaptive approach offering a wider range of acceptable indoor temperatures. Therefore, application of the adaptive approach could be one option to improve building energy performance by taking advantage of occupant adaptive behaviors during DR events. Depending on the building services and systems available, expectations of the occupants, and the control options they are given to adjust to their thermal environment, occupants could potentially adapt to temperatures higher than what are currently being practiced in buildings. However, an upper limit threshold for temperature modifications, even if temporary ones, must be recognized to minimize adverse impacts on building occupants prior to DR implementation. Therefore, the ideal goal would be to develop menthods that would identify an optimum balance between energy consumption and the building occupant thermal comfort before applying DR strategies. Prior review papers relevant to DR have generally concentrated on the energy saving potential alone, or where energy savings have been prioritized over occupant thermal comfort. This paper reviews implementation of DR from the perspective of occupant thermal comfort and presents a summary of the most relevant experimental and field studies regarding occupant thermal comfort during DR events in commercial, air-conditioned buildings. [ABSTRACT FROM AUTHOR]

Life Cycle Assessment (LCA) is increasingly used as an early-stage design-decision tool to support choices of structural system. However LCA modellers must first make numerous methodological decisions, and the resultant wide variations in approach are often inadequately described by the modellers. This paper identifies, and quantifies, the three major areas of methodological variation. These are: temporal differences in the stages considered; spatial differences in the material boundaries; and physical disparities in the data coefficients. The effects are then demonstrated through a case study of a student residential building in Cambridge. The cross-laminated timber (CLT) structure is compared with concrete frame, steel frame and load-bearing masonry, considering the influence that varying the temporal boundaries, the data coefficients, and the spatial boundaries has on the choice. While for this building CLT is confirmed as the lowest impact material, the paper demonstrates that varying the methodological choices can change the results by an alarming factor of 10 or even more. The findings confirm the need for the utmost clarity and transparency with all LCA calculations. Making wider industry or policy decisions based on LCA results should be undertaken with extreme caution. [ABSTRACT FROM AUTHOR]

When modeling the energy performance of existing buildings, model calibration always serves as an essential and necessary step to ensure the accuracy and applicability of building models. Model calibration for detailed energy models, which is also known as calibrated simulation (CS), refers to the process of tuning model's input parameters to narrow down the mismatch between the simulation result and the real-monitored data of building energy consumptions. Two major problems of current CS are: (1) a successful calibration requires HVAC domain knowledge of modelers. (2) Traditional building calibration process is labor-intensive and time-consuming. To solve these problems, a normative energy modeling based quick auto-calibration approach is proposed in this study. This approach is able to reduce the modeling time and simplify the calibration procedures. Firstly, this paper demonstrates the working principles and the credibility of Normative Energy Modeling (NEM). Then, the methodology of the proposed quick auto-calibration approach is elaborated. The energy performance model of a large hotel located in Shanghai, China with NEM is used as a case study to investigate the effectiveness of the proposed quick auto-calibration approach. The simulation result of the case study suggests that the proposed approach can significantly simplify the procedures of model calibrations while still achieve a good accuracy specified by ASHRAE Guideline 14 [1]. Besides, the results further confirm that such method requires much less time and computational power for modeling, compared with other counterparts (e.g., “Autotune” calibration [2]). With the advantage of speed and accuracy, the auto-calibration proposed in this paper is promising to be applied in both engineering and research field. [ABSTRACT FROM AUTHOR]