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Energy consumption plays an important role in contemporary economies. Its significance extends beyond utilitarian value, impacting economic robustness, environmental protection, and residents' well-being. The escalating global energy requisites necessitate efficient energy utilization and a shift towards renewable sources to address climate change and strengthen energy independence. Developing accurate predictive models to forecast long-term energy costs and savings remains a complex problem. This paper aims to provide a methodology to identify the influence of building energy performance on real estate market efficiency, focusing on property maintenance costs. Real estate plays a crucial role in human life, serving both as a fundamental need and as a vehicle for achieving personal aspirations and secure financial investments, particularly during times of economic and social instability. Through interdisciplinary methodological architecture, this study addresses three key issues: the impact of rising energy costs on market efficiency, the responsiveness of the real estate market to energy price fluctuations, and the significance of property maintenance costs on market value. The research approach includes creating and applying AI algorithms capable of evaluating extensive datasets pertaining to real estate features. Utilizing machine learning methods, the algorithm determines the importance of energy efficiency measures as well as various other inherent and external attributes of properties. The suggested methodology provides a novel approach to improve the effectiveness of market efficiency analysis. [ABSTRACT FROM AUTHOR]

This paper aims to investigate potential strategies to reduce energy consumption and environmental impacts of a recently converted rural complex into an agri-spa in a high environmental value area within the province of Viterbo (Italy). Actual operational data on appliances, climate, and energy consumption are employed for the energy audit. While this analysis generally provides energy-saving solutions, such options are not examined for their life cycle environmental impacts. The current article is based on the experimentation of a consolidated methodological approach in the scientific literature, integrating the energy audit, life cycle assessment (LCA), and economic analysis (Life Cycle Cost) to assess a series of energy-efficiency measures designed for the new wellness centre function. The combined use of these well-established procedures provides a comprehensive assessment of intervention scenarios for a particularly energy-intensive type of case study, not yet widely documented in the literature. The results reveal that energy consumption related to heating/cooling is marginal compared to the actual electrical consumption in the specific case study. Therefore, the most efficient scenarios involve installing photovoltaic systems and replacing gas boilers with high-efficiency heat pumps, especially with economically advantageous insulation. This leads to a 51% reduction in energy consumption and an 81% decrease in heating, DHW, and electricity costs. [ABSTRACT FROM AUTHOR]

The building and construction sector has a significant impact on the CO2 emissions and pollutants released into the atmosphere, which contribute to climate change. The EPDB Directive mandates the achievement of minimum energy class E for all residential buildings by 2030 and energy class D by 2033. Particularly, in Italy, about 86% of the existing building stock predates the enactment of any energy laws or regulations, making it imperative to apply the energy efficiency interventions. This paper provides a support decision tool for the identification of the standardized interventions in the building envelope, the air conditioning system, and domestic hot water production. This study is focused on a specific construction period class (1976–1990) in six different climatic zones. The methodological approach is based on a cataloguing phase and the definition of ante operam energy classes as well as on case study identification, energy requalification intervention identification, solution simulations, and cost estimation. By simulating the standardized interventions for each climatic zone, a range of possible combinations is identified. The most advantageous ones are determined based on a cost–benefit analysis considering the potential class jump achieved. The research result is a matrix of energy efficiency interventions that is applicable to each climatic zone and can be extended to the existing housing stock. [ABSTRACT FROM AUTHOR]

Digitalization offers new, unprecedented possibilities to increase the energy efficiency and improve the indoor conditions in buildings in a cost-efficient way. Smart buildings are seen by many stakeholders as the way forward. Smart buildings feature advanced monitoring and control systems that allow a better control of the buildings' indoor spaces, but it is becoming evident that the massive amount of data produced in smart buildings is rarely used. This work presents a long-term evaluation of a smart building testbed for one year; the building features state-of-the-art monitoring capability and local energy generation (PV). The analysis shows room for improving energy efficiency and indoor comfort due to non-optimal control settings; for instance, average indoor temperatures in all winter months were above 24 °C. The analysis of electricity and domestic hot water use has shown a relevant spread in average use, with single users consuming approximately four times more than the average users. The combination of CO2 and temperature sensor was sufficient to pinpoint the anomalous operation of windows in wintertime, which has an impact on energy use for space heating. Although the quantification of the impact of users on the overall energy performance of the building was beyond the scope of this paper, this study showcases that modern commercial monitoring systems for buildings have the potential to identify anomalies. The evidence collected in the paper suggests that this data could be used to promote energy-efficient behaviors among building occupants and shows that cost-effective actions could be carried out if data generated by the monitoring and control systems were used more extensively. [ABSTRACT FROM AUTHOR]

With the growing need for residential buildings as a result of population growth, the building sector is a high-priority area in climate change due to its large share of CO 2 emissions, the significant energy saving opportunities it represents, and the increasing expectations for occupant comfort. This paper presents the results of climate change impact assessment on a typical mid-rise residential building in four representative cities with different climatic characteristics in Turkey. Three different scenarios that characterize different solutions towards space cooling are developed, from naturally ventilated to fully air-conditioned. The energy requirements and corresponding CO 2 emissions due to space conditioning as well as occupant thermal comfort are the investigated building performance metrics. The preliminary results based on energy simulations indicate that pronounced overheating will be experienced in the future, which will have a strong effect on cooling energy use and/or occupant comfort. The projected energy demand also points to the future need to explore measures for climate change adaptation of buildings and the importance of the decarbonization of the electricity industry for climate change mitigation. The analysis results presented in this paper provide a basis for future studies on building retrofit for climate change. • Evaluation of climate change impact on a typical residential building in Turkey. • Cooling is to dominate thermal loads due to increased temperatures. • Warmer climates to be influenced most by negative effects. • Severe overheating for poorly ventilated, highly insulated buildings. • Adaptation measures needed for climate-proofing existing buildings. [ABSTRACT FROM AUTHOR]

Inequities in the built environment have long persisted in communities around the world. Prior studies have exposed that energy retrofit programs invest inequitably in residential buildings, leaving racial minorities with disproportionately less energy inefficient homes, regardless of income level. This inequity in energy performance and retrofit investments has yet to be studied for commercial and institutional buildings. Further research has shown that tree canopy coverage and vegetation is less dense in historically disadvantaged communities, contributing to a greater urban heat island effect and corresponding higher energy consumption for cooling compared to historically advantaged communities. Moreover, there is limited albeit emerging research that utilizes granular socioeconomic data with fine temporal scale energy consumption data to help identify energy inequalities among race and income groups. This paper examines the differential impact of energy efficiency retrofit installation on energy performance between advantaged vs. disadvantaged groups using data from U.S. public school buildings. Additionally, it studies how the effect of retrofits is compounded by the presence of vegetation surrounding the building. Utilizing hourly interval smart meter data from primary and secondary school buildings in California, the impact of retrofits are quantified using three energy metrics. Then the disparities in retrofit impact for advantaged vs. disadvantaged groups (as defined by three equity metrics) are evaluated using school-level demographic data. Lastly, regression on the percent change in monthly energy consumption from the pre-retrofit to post-retrofit period is used to quantify the incremental benefits of retrofits compounded by vegetation. Findings show that energy efficiency retrofits can improve energy performance and reduce energy inequities. Specifically, retrofits significantly increase energy efficiency and reduce the energy efficiency equity gap by up to 198%. Evidence shows that vegetation surrounding a building has a synergistic effect with mechanical retrofits for reducing energy consumption in respect to reduced cooling loads, even in cases where mechanical retrofits alone resulted in significant increases in energy consumption. Dense vegetation provides around 6% energy savings, similar in magnitude to savings from mechanical retrofit installation. This validates the effectiveness of retrofits for reducing energy consumption and improving energy equity, and demonstrates the ability of vegetation to provide additional energy savings beyond mechanical interventions. [ABSTRACT FROM AUTHOR]

In Jordan, the residential sector is responsible for 43% of all power use and 21% of total energy usage. The use of energy for space heating and cooling accounts for more than 60% of residential energy use. The computer-generated residence, a typical one-storey Jordanian home with a functional floor area of 186 m2, is located in Amman and was modelled to be more energy efficient by incorporating renovating techniques to lower heating and cooling demand, attain marginal energy demand, and produce a high-quality indoor living environment. To get at the idea of passive design houses as appropriately depicted in this paper, various passive design tactics and methodologies were employed. Using DesignBuilder simulation software, Revit modelling software, solar energy analysis software, and assessment of the total energy consumption before and after the deployment of passive design approaches, the effects of each retrofit method were evaluated. The results of this study show that the yearly energy savings for heating is about 78%, and the indoor air quality and temperature of the residence can be significantly enhanced compared to its original situation. The total employed energy in the virtual building was scaled down from 56.57 kWh/m2 to 15.25 kWh/m² each year. [ABSTRACT FROM AUTHOR]

Several studies have demonstrated the capability of data-driven modelling based on on-site measurements to characterise the thermal performance of building envelopes. Currently, such methods include steady-state and dynamic heating experiments and have mainly been applied to scale models and unoccupied test buildings. Nonetheless, it is proposed to upscale these concepts to characterise the thermal performance of in-use buildings based on on-board monitoring (OBM) devices which gather long-term operational data (e.g., room temperatures, gas and electricity consumption...). It remains, however, to be proven whether in-use data could be a cost-effective, practical and reliable alternative for the dedicated tests whose more intrusive measurements require on-site inspections. Furthermore, it is presently unclear what the optimal experimental design of the OBM would be and which data analysis methods would be adequate. This paper presents a first step in bridging this knowledge gap, by using on-board monitoring data to characterise the overall heat loss coefficient (HLC) [W/K] of an occupied, well-insulated single-family house in the UK. With the aid of a detailed building physical framework and specifically selected data subsets a sensitivity analysis is carried out to analyse the impact of the measurement set-up, the duration of the measurement campaign and the applied data analysis method. Although the exact HLC of the building is unknown and no absolute errors could hence be calculated, this paper provides a new understanding of the decisions that have to be made during the process from design of experiment to data analysis. It is demonstrated that such judgements can lead to differences in the mean HLC estimate of up to 89.5%. [ABSTRACT FROM AUTHOR]

Stakeholders such as urban planners and energy policymakers use building energy performance modeling and analysis to develop strategic sustainable energy plans with the aim of reducing energy consumption and emissions from the built environment. However, inconsistent energy data and the lack of scalable building models create a gap between building energy modeling and traditional planning practices. An alternative approach is to conduct a large-scale energy usage survey, which is time-consuming. Similarly, existing studies rely on traditional machine learning or statistical approaches for calculating large-scale energy performance. This paper proposes a solution that employs a data-driven machine learning approach to predict the energy performance of urban residential buildings, using both ensemble-based machine learning and end-use demand segregation methods. The proposed methodology consists of five steps: data collection, archetype development, physics-based parametric modeling, machine learning modeling, and urban building energy performance analysis. The devised methodology is tested on the Irish residential building stock and generates a synthetic building dataset of one million buildings through the parametric modeling of 19 identified vital variables for four residential building archetypes. As a part of the machine learning modeling process, the study implemented an end-use demand segregation method, including heating, lighting, equipment, photovoltaic, and hot water, to predict the energy performance of buildings at an urban scale. Furthermore, the model's performance is enhanced by employing an ensemble-based machine learning approach, achieving 91% accuracy compared to the traditional approach's 76%. Accurate prediction of building energy performance enables stakeholders, including energy policymakers and urban planners, to make informed decisions when planning large-scale retrofit measures. • Utilization of ensemble-based machine learning modeling. • Implementation of an end-use demand segregation. • Identification of key building features for each end-use demand prediction. • Analysis of the impact of retrofit measures using future weather data. [ABSTRACT FROM AUTHOR]

• A BIM-supported energy performance analysis for green building design is proposed. • This systematic analytical framework integrates explainable machine learning and multi-objective optimization. • It can serve as a data-driven decision tool to guide green building development even under uncertainty. • The top important features that greatly affect the building energy performance can be quantitatively identified. • Building energy performance can be improved by 13.43% using the obtained optimal strategy. Supported by the combination of the advanced BIM technique with intelligent algorithms, this paper develops a systematic framework using explainable machine learning and multi-objective optimization to realize the automatic prediction and optimization of building energy performance towards the sustainable development goal. There are three critical parts incorporated, including the DesignBuilder simulation, BO-LGBM (Bayesian optimization-LightGBM) and an explainable method SHAP (SHapley Additive explanation)-based prediction and explanation of building energy performance, and AGE-MOEA algorithm-based multi-objective optimization (MOO) under sources of uncertainty. It has been verified in a case study for green building design. Results show that: (1) The predictive BO-LGBM model can make a highly precise prediction in agreement with the simulation data, reaching up the R2 larger than 93.4% and MAPE smaller than 2.13%. From the SHAP calculation, features related to the HAVC (Heating Ventilation and Air Conditioning) system tend to contribute more to affecting the prediction results. (2) The AGE-MOEA-based optimization can identify a set of Pareto optimal solutions in simultaneously minimizing the building energy consumption, CO 2 emission, and indoor thermal discomfort degree, arriving at the highest optimization rate of 13.43% under proper adjustment of certain features. (3) In the MOO task, the consideration of model and data uncertainty by prediction intervals and Monte-Carlo simulation can further increase the optimization rate by around 4.0% than the deterministic scenario, resulting in more desired strategies in optimizing the green building performance. In short, this paper enriches the area of green building development. For one thing, it raises the transparency and interpretability of machine learning to make the prediction more convincing. For another, it efficiently determines the passive and active design solutions along with the detailed profile of influential factors for green building design. [ABSTRACT FROM AUTHOR]

Urban expansion, driven by population and economic growth, has been a major contributor to increased levels of energy consumption across the globe. Beirut, Lebanon's capital, is no exception in facing a surge in its demand for electricity as it expands. However, with frequent power outages, underpinning a largely problematic power sector, Beirut's demand for electricity is becoming a real hurdle that impedes the city's economic growth and development. This paper introduces a near-city-scale building energy model, BEirut Energy Model BEEM, which estimates the building stock's electricity consumption in two different districts in Beirut. The methodology uses rule-based expert data for an archetypal classification of the buildings based on their functions and periods of construction with their corresponding attributes including the number of floors, number of apartments, and bimonthly electricity consumption to generate a 3D model for 3630 buildings coupled to the hourly weather conditions and topographic map, which is then simulated in EnergyPlus. The predicted consumption of 2311 buildings is then calibrated with actual available metered data, to adapt the model to Beirut's occupancy and users' behaviors. Calibrated results are mapped to reveal the spatiotemporal distribution of energy peak demands which provide insights for future interventions. An analysis of the spatial distribution of electricity use demonstrates a spatial clustering that underlies urban energy demand which can be used for smart grid zoning. [ABSTRACT FROM AUTHOR]

Implementing Nature-Based Solutions (NBS) is a promising way to reduce building energy demand and facilitates the achievement of United Nations Sustainable Development Goal 7, as they provide shading, evapotranspiration cooling and other influences on buildings. Although this field has attracted much attention, uncertainty persists regarding the potential of different NBS types to impact building energy demand in different climate conditions. To clarify this uncertainty, 101 papers were studied based on the Web of Science and Scopus databases. The current status analysis explored the development state of this field. Building energy performance analysis evaluated the potential reduction in cooling and heating energy in different climates by applying different NBS types at building scale. The review revealed that the cooling energy saving potential of NBS varies from 3% to 90%, while the potential reduction in heating energy demand ranges from 0.58% to 60%. The extent of the reduction in both cases is dependent on the NBS type and climate. Notably, some NBS types may lead to an increase in heating energy demand by between 5.9% and 25% in climates with short and mild winters. This review found that maximizing the energy-saving potential of NBS requires a comprehensive consideration of multiple factors rather than maximizing an individual factor. Further, most studies in this field have only concentrated on a few NBS types and climate zones, resulting in significant differences in research depth among different NBS categories. Future work should focus on neglected NBS types and climates to fully understand their energy-saving potential. • Different types of nature-based solutions regarding building energy demand reduction were reviewed. • Climate zone dependency of NBS impact on building energy demand was analyzed and quantified. • Influential factors to optimize the impact of nature-based solutions on building energy performance were identified. • Research gaps regarding nature-based solutions impact on building energy demand were identified. [ABSTRACT FROM AUTHOR]

The building sector accounts for over one-third of global energy consumption and a significant share of carbon emissions. Improving building energy efficiency, particularly in the tropics, where most of the future development is foreseen, is thus crucial. One of the main factors that determine building energy performance is the building envelope. This paper presents a comprehensive review of the impact of several envelope design variables, such as thermal, optical, physical, and geometrical, on the energy performance of buildings in the tropics. A correlational analysis that elaborates on the effectiveness of each of these measures is discussed in detail. The findings indicate that insulation, glazing properties, and Window-to-wall ratio (WWR) are promising solutions for improving energy efficiency in tropical buildings. Among these, insulating the building envelope has the most significant impact on energy savings. The variables of building orientation and the thermal mass of building materials have the least influence on total energy consumption. The latter exhibits complex effects in regions characterized by hot-humid climates. An optimized design in cooling-dominated climates should have a large aspect ratio, a higher WWR in the north and south facades, and an ideal shading system. With all variables combined and appropriate ventilation, a building can save 35% of annual energy and up to 60% in some cases. This study further reveals that available research on the effect of building shape and the context of surrounding built environment on energy consumption remains limited. • Combining all envelope variables can provide annual average savings of 35%. • Average savings can drop by over 20% without insulation. • Insulation, WWR, and glazing type are the most promising solutions in Tropics. • Use insulation based on WWR, shading, thermal mass, and glazing type. • Shading, glazing, SHGC, and WWR have been mainly studied together. [ABSTRACT FROM AUTHOR]

[Display omitted] • Present a comprehensive review of building electricity profile models. • Distinguish the differences of building electricity profile and prediction models. • Categorize the statistical and bottom-up models for electricity use profiles. • Investigate the validation metrics in accordance with the modeling methods. • Discuss the key future perspectives of building electricity profile analysis. The building sector contributes significantly to overall energy consumption and carbon emissions. Improving renewable energy utilization in buildings is of considerable importance for their carbon neutrality. Electricity is expected to become the major form of final energy consumption in buildings; as such, understanding the features of building electricity use profiles is pivotal for building energy system design and city-scale electricity distribution network planning. This study reviews the current status and future challenges of building electricity use profile models. The paper distinguishes between electricity use profile models and electricity use prediction models. Then it comprehensively reviews current research papers, focusing on four aspects, including the temporal and spatial distribution of datasets, the distinction between statistical and bottom-up modeling methods, mathematical and engineering domain validation metrics, and applications in building energy system design and energy policy making. Key perspectives for future research are discussed in the paper, including the temporal scales of models, the aggregation of electricity use profiles, occupant-driven environment-interactive model structures, as well as the fit-for-purpose model evaluation. This is intended to inform and inspire future research on building electricity use profiles, improve the in-depth understanding and quantitative descriptions of building electricity use, and support the design of future building systems and energy distribution networks. [ABSTRACT FROM AUTHOR]

• An air source heat pump is coupled to a forced ventilated PV double skin façade. • Building thermal consumption is electrified and renewable energy promoted. • A mathematical quasi-static model is developed to evaluate the energetic behaviour. • The system thermal/electricity energy generation and consumption is analysed. • An economic evaluation is made to obtain the investment and payback of the system. To spread the nearly Zero Energy Building (NZEB) concept, there is a need for the combined integration of energy saving measures and energy supply systems that minimize the non-renewable primary energy consumption. This paper aims to analyse the capabilities of a novel system composed of a photovoltaic (PV) double skin façade (PV-DSF) coupled to an air source heat pump system (ASHP). The main goal of this system is to provide heating and domestic hot water (DHW) using renewable energy. A quasi-steady mathematical model has been developed to assess the energy capabilities of the proposed system. The thermal and electric generation of the system can be estimated with the hourly outdoor temperature and solar radiation as input data. Calculations have been carried out on an existing block of flats in Bilbao (Spain) to estimate the energy viability of the proposed system. It has been proved that almost all the thermal energy demand can be supplied with the ASHP system, which improves its Seasonal Performance Factor (SPF) in 14.8%. Regarding electric energy, the PV-DSF panels can supply approximately 70% of the electricity consumed by the ASHP system and the fans of the PV-DSF. In addition, if more PV modules are installed on the roof, the demand can be covered with a surplus for other uses. Economically, comparing it with a conventional natural gas boiler facility, the investment cost is amortized in 6.4 years. [ABSTRACT FROM AUTHOR]

Highlights • Create city-specific energy performance grading model using city disclosure data. • Method to grade buildings that is interpretable, reproducible, scalable, and robust. • Develop a clustering algorithm to grade buildings on performance. • Validated approach demonstrates significant improvements over current methods. Abstract As the effects of anthropogenic climate change become more pronounced, local and federal governments are turning towards more aggressive policies to reduce energy use in existing buildings, a major global contributor of carbon emissions. Recently, several cities have enacted laws mandating owners of large buildings to publicly display an energy efficiency rating for their properties. While such transparency is necessary for market-driven energy reduction policies, the reliance on public-facing energy efficiency grades raises non-trivial questions about the robustness and reliability of methods used to measure and benchmark the energy performance of existing buildings. In this paper, we develop a building energy performance grading methodology using machine learning and city-specific energy use and building data. Leveraging the growing availability of data from city energy disclosure ordinances, we develop the GREEN grading system: a framework to facilitate more accurate, fair, and contextualized building energy benchmarks that account for variations in the expected and actual performance of individual buildings. When applied to approximately 7500 residential properties in New York City, our approach accounts for the differential impact of design, occupancy, use, and systems on energy performance, out-performing existing state-of-the-art methods. Our model and findings reinforce the need for more robust, localized approaches to building energy performance grading that can serve as the basis for data-driven urban energy efficiency and carbon reeduction policies. [ABSTRACT FROM AUTHOR]

In recent years, many cities have adopted energy disclosure policies to better understand how energy is consumed in the urban built environment and how energy use and carbon emissions can be reduced. The diffusion of such policies has generated large-scale streams of building energy data, creating new opportunities to develop the fundamental science of urban energy dynamics. Nevertheless, there is limited research that rigorously analyzes building energy performance patterns over time. This paper provides a comprehensive framework to analyze building energy time series data and identify buildings with similar temporal energy performance patterns. We use data from approximately 15,000 properties in New York City, covering a six-year reporting period from 2011 to 2016. After pre-processing and merging the data for each constituent year, we use an unsupervised learning algorithm to optimally cluster the energy time series and statistical tests and supervised learning methods to infer how building characteristics vary between clusters. Our results show that energy reductions in New York City are mainly driven by its commercial building stock, with larger, newer, and higher-value buildings demonstrating the largest improvements in energy intensity over the study period. Moreover, voluntary energy conservation schemes are found to be more effective in boosting energy performance of commercial properties, compared to residential buildings. Our results suggest two distinct temporal patterns of energy performance for commercial and residential buildings, characterized by energy use reductions and increases. This finding highlights the differential response to energy reporting and disclosure, and presents a more complex picture of energy use dynamics over time when compared to previous studies. In order to realize significant energy use improvements over time and reach energy and carbon reduction goals, cities need to design and implement comprehensive energy policy frameworks, bringing together information transparency and reporting with targeted mandates and incentives. [ABSTRACT FROM AUTHOR]

In the perspective of reducing land occupation of built environments, the most efficient strategy is to increase the reuse of obsolete or abandoned constructions instead of designing new buildings. This is particularly true regarding public buildings, usually characterized by considerable dimensions. The paper is focused on this issue, particularly on its energy implications. It analyzes the headquarters of the 'G. D'Annunzio' University in Pescara, Italy, initially designed for commercial use and later converted to a university building during its construction. The authors first carried out a numerical analysis of the building energy performance in its current state and then they proposed some improvement interventions evaluating their incidence on the annual energy balance of the building, showing how the most commonly used energy saving strategies do not give the best results in any case, being greatly influenced by the climatic zone in which the building is located. Starting from these considerations, the authors carried out the analysis by considering three different Italian climatic conditions so as to determine their influence on the building energy performance and to define the best strategy for energy requalification in each climatic zone. [ABSTRACT FROM AUTHOR]

The energy retrofitting of existing buildings is of major importance to reach the energy sustainability target set by the European Union (EU) for 2020. Innovative retrofitting solutions can involve the adoption of Phase Change Materials (PCMs), but an effective use of PCM in buildings requires an appropriate selection of the thermo-physical properties, quantity and position of the PCMs. To guarantee a good functioning of a PCM and ensure economic feasibility, an optimisation of PCM use is advisable. In the present paper, multi-objective optimisation analyses for the energy retrofitting of office buildings with PCM-enhanced opaque building envelope components are presented. A retrofitting intervention on either the external or internal side of the opaque envelope was considered, and a maximum of two PCM layers with different melting temperatures were selected and placed in different positions within the wall. Two sets of objective functions were minimised; first, primary energy consumption and global costs, and then the building energy needs for heating and cooling and investment costs. The search variables included the thickness and thermo-physical properties of the PCM layers, the window type, the insulation and internal lining materials, the wall configuration and U-value. In order to provide a robust methodology to drive designers towards an informed choice of the final retrofitting strategy, post-optimisation analyses were additionally carried out to investigate the variable values that led to the optimal solutions. Interesting and non-trivial information was obtained. The optimal thermo-physical properties of PCMs were found to be affected in particular by the operation of the HVAC system. [ABSTRACT FROM AUTHOR]

The building sector accounts for significant portion of total final energy use in most countries. One way to reduce significantly building energy consumption is to adopt energy efficiency technologies and strategies. Due to environmental concerns and high cost of energy in recent years there has been a renewed interest in building energy efficiency. Passive House (PH) standard provides a cost-efficient way of minimizing energy demand of buildings in accordance with global principle of sustainability. However, it is ongoing challenge to improve building energy efficiency without compromising indoor environmental quality. The objective of this paper is to review the interactions between energy performance and indoor environment quality in buildings with PH standard, considering parametric sensitivity study including effects of diverse parameters on PH building performance. Passive House technologies including heat and cooling recovery ventilation, pre heating/cooling fresh air, pre-ventilation and passive cooling are presented considering different weather conditions. Thermal comfort represented by recommended thermal range and comfort zone and indoor air quality indicated by CO2 concentration have been numerically and experimentally analysed. The simulation and measurement results illuminate that it is possible to realize energy efficiency and favourable indoor environment in PH buildings at the same time. [ABSTRACT FROM AUTHOR]

Enabling robust energy benchmarking to reliably locate performance inefficiency for upgrading is critical to the success of building retrofitting programs in building sector. Multi-criteria benchmarking is emerging as a more rational option over the traditional single-angle method to assess building performance which is fundamentally of multi-factor nature. Particularly, with its easier concept, the compromise Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) based multi-angle benchmarking appears attractive. Nevertheless, existing TOPSIS based procedures tend to ignore the common issue of multicollinearity trap which could result in misleading decisions. Meanwhile, variable clustering renders an empirical alternative for handling multicollinearity with high traceability. Combining with information-oriented Shannon entropy, this paper develops an iterative Clustering around Latent Variables (CLV) based objective entropy weighted TOPSIS approach for benchmarking building energy performance in a multi-factor manner. It essentially integrates the benefits of variable clustering to address multicollinearity with information theory for objective weighting on decision attributes in order to pursue TOPSIS benchmarking accuracy. A 324-dwelling case shows the robustness of the constructed procedure in a temporally dynamic context. [ABSTRACT FROM AUTHOR]

The variable air volume (VAV) system is the most popular form of heating, ventilation, and air-conditioning (HVAC) system used in commercial buildings. Researchers and engineers often use VAV systems as a reference when evaluating new technologies and systems or comparing design options. However, VAV system performance varies significantly, in part because of variations among VAV system controls, so, when analyzing use cases, it is critical to accurately represent system controls in order to accurately define system performance. Unfortunately, no existing literature documents standard VAV system controls for this purpose. This paper aims to remedy this omission by characterizing the variations in VAV system controls and proposing an approach to representing VAV system baseline performance. We used EnergyPlus to model variation among VAV system controls. We use the medium-size office reference-building model developed by the U.S. Department of Energy to demonstrate the impact of variations among controls in two U.S. climate zones and sort system performance into “good,” “average,” and “poor” categories. [ABSTRACT FROM AUTHOR]

• An integrated energy-economic analysis of Net ZEB office buildings was proposed. • A thorough discussion on the energy balance was provided for Italian climate zones. • Energy grid integrations, self-consumption, storage and losses were analyzed. • The cost assessment of office buildings was performed and compared to state-of-art. • The energy-economic benefit of a new Net ZEB office in Italy was demonstrated. Recently, the built environment has become one of the leading sectors in the challenge of decarbonization and energy consumption reduction. Because of the specific use and the complexity of the typological, architectural, and technical characteristics, non-residential buildings represent a challenge to overcome the nearly Zero Energy Building standard and to achieve the objective of Zero or Net Zero Energy Buildings. It is desirable that the energy produced in situ from renewable sources totally covers the energy demand (ZEB); alternatively, the amount of energy self-produced and dispatched into the grid must be greater than (or equal to) the energy withdrawn from the grid (Net ZEB). At the same time, it is necessary not to neglect the economic aspects. The aim of this work is to propose an integrated energy-economic analysis to demonstrate the actual feasibility of new Net ZEB offices in different Italian climate zones. Thus, the energy analysis of a reference building, based on consolidated and cost-effective envelope and HVAC systems, has been carried out through dynamic simulations. The photovoltaic field located on the roof provides the energy needed for lighting, appliances, and HVAC. The obtained results are the energy demand and the energy production from renewable sources for the case study in each Italian climate zone. Moreover, the paper discusses the complexity of the annual energy balance, considering contributions such as grid integrations, self-consumption, energy stored and losses. Finally, the cost assessment of the proposed building has been carried out and compared to the state-of-art, pointing out the economic feasibility of the Net ZEB. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a powerful simulation-based multi-objective optimization of building energy efficiency and indoor thermal comfort to obtain the optimal solutions of the comfort-energy efficient configurations of building envelope. The optimization method is developed by integrating a multi-objective artificial bee colony (MOABC) optimization algorithm implemented in MATLAB with EnergyPlus building energy simulation tool. The proposed optimization approach is applied to a single office room; and the building parameters, including the room rotation, window size, cooling and heating setpoint temperatures, glazing and wall material properties are considered as decision variables. In the present study, single-objective and multi-objective optimization analyses of the total annual building electricity consumption and the Predicted Percentage of Dissatisfied (PPD) are investigated to bring down the total energy cost as well as the thermal discomfort in four major climate regions of Iran, i.e. temperate, warm-dry, warm-humid and cold ones. In the results part, the achieved optimal solutions are presented in the form of Pareto fronts to reveal the mutual impacts of variables on the building energy consumption and the thermal discomfort. Finally, the ultimate optimum solution on the Pareto fronts are selected by TOPSIS decision-making method and the results of double-objective minimization problem are compared with the single-objective ones as well as the base design. The results of double-objective optimization problem indicate that in different climates, even though the total building electricity consumption increases a bit about 2.9–11.3%, the PPD significantly decreases about 49.1–56.8% compared to the baseline model. In addition, the comparisons of single-objective and double-objective optimization approaches clearly show that multi-objective optimization methods yield more appropriate results respect to the single ones, mainly because of the lower deviation index value from the ideal solution. [ABSTRACT FROM AUTHOR]

This paper presents an investigation of cool-roof impacts on thermal and energy performance of a School building located in Athens, Greece. The research work refers to a month recording of thermal performance indicators (TPIs) in selected classrooms, namely indoor air- roof- and ceiling surface- temperature. Thermal inspections also included roof thermal-camera scanning and reflectance measurements of exterior building surfaces. Thermal impacts were assessed in terms of comparisons of TPIs with the ones obtained by the same procedure in a nearby twin building. The impact of the cool roof on building energy performance and thermal comfort was assessed through dynamic energy simulations. Model calibration is achieved through comparisons of simulated indoor air temperature with the measured one in both the ex-ante and ex-post condition. Two summer-conditioning cases were analyzed, namely “ceiling fans with no cooling systems” (actual condition) and “air-conditioning split units with no ventilation systems”. For the building block below the cool roof it was found that: in the former case, the energy consumption for ventilation is reduced by at least 25% with an up to 3% increase of comfort operating hours, at the expense of a 12% heating penalty in winter. In the latter case, the energy consumption for cooling is reduced by at least 18% with a similar increase of comfort hours, compensating energy-consumption increase for heating in winter. It is concluded that the cool roof is an efficient solution for School buildings in warm climates as it improves summer thermal comfort and ensures annual energy savings when heat pumps are used for cooling purposes. [ABSTRACT FROM AUTHOR]

Inter-building effect is responsible for affecting buildings’ primary energy requirement for heating, cooling, and lighting. Nevertheless, the impact of natural ventilation should also be considered while predicting building energy demand, since it is documented to substantially affect indoor environmental quality and thermal comfort. This paper investigates the impact of natural ventilation on building primary energy requirement prediction. The Inter-Building Effect (IBE) approach is applied in a typical residential block in Italy. A sensitivity analysis is performed to determine the key input parameter among (i) climate boundary, (ii) infiltration rate, (iii) opening percentage, and (iv) wind strength. Two scenarios, i.e. the stand-alone building and the same building surrounded by its neighborhood, are compared. The thermal-energy dynamic simulation of the different scenarios is carried out to investigate the impact of outdoor airflows on building primary energy requirement for heating and cooling and on indoor thermal comfort, investigated by means of the Thermal Deviation Index method. Inaccuracies in energy need prediction imputable to natural ventilation without taking into account the IBE are detected. The findings show that IBE is much more affected by buildings’ opening percentage, infiltration rate, and local wind compared to the weather context since IBE is a local microscale phenomenon. [ABSTRACT FROM AUTHOR]

• A top-down analysis of the non-residential building stock in Brazil is proposed. • The share of each building typology in the country energy consumption was assessed. • A statistical analysis showed variation of EUI according to type of building and region. • Reference values for the commercial building stock in Brazil is provided. This paper provides a pioneering, top-down analysis of the non-residential building stock in Brazil. A country-wide dataset was used, comprising 10,000 buildings divided into 12 typologies. Key indicators include floor area, energy use intensity, and operation patterns. In addition, predominant wall constructions, lighting systems, equipment, water heating, and HVAC systems for each typology were identified, including average density power for the systems. Data from the Brazilian Energy Balance report was correlated with the sample of buildings to determine the proportion of each building typology in the total energy consumption of the country, considering different energy sources. This study provides an innovative and up-to-date status of the characteristics of non-residential buildings in Brazil. A complementary statistical analysis addressed the correlation between energy use intensity and the building characteristics including an ANOVA test and a regression analysis in order to investigate the influence of the energy usage indicators (Lighting Power Density, Equipment Power Density, Cooling capacity) on the energy use intensity of each building typology in Brazil. This study is intended to serve as a reference for building characteristics and building energy performance in Brazil, and hence support diverse research regarding country-level building stock modeling, energy benchmarking, climate change impacts, and energy-efficiency related studies. [ABSTRACT FROM AUTHOR]

Climatic zoning for buildings is an important topic in the field of building energy efficiency. Defining climatic zones is challenging, as it involves many variables sparsely distributed in time and space. Many methods exist today to define climatic zoning, most of them focused on weather-centred definitions. The actual response of buildings in terms of performance is rarely considered in the climatic zoning definition, leading to significant levels of misclassification. Early initiatives of performance-based climatic zoning show the large potential of this approach. However, these initiatives have shortcomings on handling complex building-stocks, defining boundaries between zones, and quality-assurance of results. This paper proposes a performance-based approach for climatic zoning addressing these shortcomings, relying on the intensive use of archetypes, building performance simulation, and GIS. The method was tested in south-eastern USA, using simulation results for 52 building models from the USA Department of Energy (DOE) building stock for 95 locations. Results were clustered using the k-means method considering 3 different zoning levels. An existing climatic zoning performance indicator, the Mean Percentage of Misclassified Areas (MPMA), was calculated for each alternative. The best-case scenario (3-zones) reached less than 2% of MPMA, which is low when compared with existing methods (10% for ASHRAE169-2013, 15% for ASHRAE169-2009). • A performance-based approach for climatic zoning is proposed. • It provides flexibility to adopt different performance metrics and building types. • Different weather data sets can be adopted including current and future scenarios. • The mismatch between climatic zoning and building performance was reduced. • Misclassification was lowered to 2% against 10% and 15% of the Degree-days method. [ABSTRACT FROM AUTHOR]

The paper presents a review on major contributions in infrared thermography to study the built environment at multiple scales. To elaborate the review, hundreds of studies conducted between the 1980s and 2020s were first selected based on their relevance to the scope. Afterward, the most relevant contributions were classified and chronologically sorted. From the classification, it is observed that most reviewed studies were conducted to evaluate the thermal performance of buildings or detect their defects using images collected by an infrared camera. At the same time, a considerable number of studies used thermal images obtained by a satellite to observe the urban heat island effect. Despite the important number of contributions in infrared thermography at multiple scales of the built environment, three main research gaps or opportunities can be identified in the literature. First, it would be possible to perform a more detailed analysis of urban heat fluxes using thermal images collected at multiple scales. Then, thermal images collected by a mounted or handheld infrared camera could be used to create building energy models. Finally, better visualization tools would be developed to monitor a city's energy use and improve its sustainability if thermal images were integrated into Internet-of-Things and digital twin platforms. [Display omitted] • A review was performed on infrared thermography cutting across multiple scales. • Urban heat fluxes can be studied in more detail using multi-scale thermal images. • Multi-scale thermal images could describe urban building energy models better. • Building energy efficiency could be assessed by linking thermal images and IoT data. • Thermal images are not considered as geospatial data and included in digital twins. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient methodology for the simulation-based multi-objective optimization problems, which addresses important limitations for the optimization of the building energy performance. In this work, a mono- and multi-objective particle swarm optimization (MOPSO) algorithm is coupled with EnergyPlus building energy simulation software to find a set of non-dominated solutions to enhance the building energy performance. To evaluate the capability and effectiveness of the approach, the developed method is applied to a single room model, and the effect of building architectural parameters including, the building orientation, the shading overhang specifications, the window size, and the glazing and the wall material properties on the building energy consumption are studied in four major climatic regions of Iran. In the optimization section, mono-criterion and multi-criteria optimization analyses of the annual cooling, heating, and lighting electricity consumption are examined to understand interactions between the objective functions and to minimize the annual total building energy demand. The achieved optimum solutions from the multi-objective optimization process are also reported as Pareto optimal fronts. Finally, the result of multi-criteria minimization is compared with the mono-criterion ones. The results of the triple-objective optimization problem point out that for our typical model, the annual cooling electricity decreases about 19.8–33.3%; while the annual heating and lighting ones increase 1.7–4.8% and 0.5–2.6%, respectively, in comparison to the baseline model for four diverse climatic regions of Iran. In addition, the optimum design leads to 1.6–11.3% diminution of the total annual building electricity demand. The proposed optimization method shows a powerful and useful tool that can save time while searching for the optimal solutions with conflicting objective functions; therefore facilitate decision making in early phases of a building design in order to enhance its energy efficiency. [ABSTRACT FROM AUTHOR]

The proposed energy performance diagnosis is intended to identify poor energy performance in a building and pinpoint the causes to provide suggestions for building operators to implement timely repair and maintenance. Many previous studies have probed the complicated problem of building energy performance diagnosis to achieve energy conservation and better performance. However, few of them have been successful because most of these methods rely on a large amount of data from an Energy Management and Control System (EMCS), and these data are unreliable. A detailed description of the methodology based on energy consumption data is presented in this paper along with the development of a prototype integrated toolkit. Weekly, daily and hourly diagnoses are developed at the whole building level, system level and component level, respectively. To validate the feasibility and applicability of the method, a case study on an office building demonstrating the proposed method was completed and was able to detect underperformance operation and energy waste. [ABSTRACT FROM AUTHOR]

This paper presents the development of a data driven probabilistic graphic model to predict building energy performance. A directed graphical model, namely, a Bayesian Networks (BNs) model is created. Each node in the BNs represents a random variable such as outside air temperature and energy end use. The links between the nodes are probabilistic dependencies among these corresponding variables. These dependencies are statistically learned and/or estimated by using measured data and augmented by domain expert knowledge. BNs models became popular models in the last decade and only recently received attention for HVAC (Heating, Ventilation and Air-conditioning) applications. A case study of using a BNs model to predict HVAC hot water energy consumption in an office building is presented. The energy estimation results meet with the criteria recommended by ASHRAE Guideline 14. This case study also shown that the discretized Bayesian Network model is sensitive to the discretization policy (i.e., bin size selection) employed. The applicability of a BNs model becomes questionable outside the range in which the model is learned. [ABSTRACT FROM AUTHOR]

This paper describes the development and focus in the analysis of the Brazilian metamodels used in the residential labeling system. The metamodels represent the thermal behavior of naturally ventilated and artificially air-conditioned residential buildings. A computational simulation experiment using the EnergyPlus program was conducted taking into account naturally ventilated and artificially air-conditioned residential buildings. Several parameters were considered for parametric simulations such as thermal transmittance, thermal solar absorptance, shading device, ventilation opening factor. The multi-linear regression method was used to predict the cooling degree hours and the annual energy consumption for heating and cooling for dormitories and living room. An assessment on the accuracy of the Brazilian metamodels for residential buildings was analyzed and showed that the regressions include standard errors that may influence the metamodels accuracy. Therefore, artificial neural networks (ANNs) are presented as an alternative to improve the metamodels used in the Brazilian regulation. Comparing both statistical method multi-linear regression and artificial neural network method it can be seen that ANN presented lower results for standard deviation end root mean square errors. Therefore, ANNs are recommended to improve the accuracy of the prediction in further behavior associated with the labeling system. [ABSTRACT FROM AUTHOR]

Buildings were responsible for over 30% of total Chinese energy consumption in 2010. With a wild range of climate conditions and a separate air conditioning structure in campus buildings, it is significant but difficult to assess the energy performance of campus buildings in China. This paper proposes a novel mechanism to assess energy performance of campus buildings, using a fuzzy analytic network process (FANP). Six criteria are used as key performance indicators based on climate conditions in China and the air conditioning device deployment structure in campus buildings: 1) building envelope; 2) renewable energy; 3) illumination; 4) thermal comfort; 5) window and door; and 6) occupation status. A simulation is presented to illustrate the effectiveness of the proposed approach, analyzing three different rooms in the International School of Software, at Wuhan University. Assessment results are discussed in terms of optimizing future energy performance of campus buildings. [ABSTRACT FROM AUTHOR]

Being able to identify detailed meta factors of energy performance is essential for creating effective residential energy-retrofitting strategies. Compared to other benchmarking methods, nonparametric multifactor DEA (data envelopment analysis) is capable of discriminating scale factors from management factors to reveal more details to better guide retrofitting practices. A two-stage DEA energy benchmarking method is proposed in this paper. This method includes (1) first-stage meta DEA which integrates the common degree day metrics for neutralizing noise energy effects of exogenous climatic variables; and (2) second-stage Tobit regression for further detailed efficiency analysis. A case study involving 3-year longitudinal panel data of 189 residential buildings indicated the proposed method has advantages over existing methods in terms of its efficiency in data processing and results interpretation. The results of the case study also demonstrated high consistency with existing linear regression based DEA. [ABSTRACT FROM AUTHOR]

Many cities today are committed to increase the energy efficiency of buildings and the fraction of renewables especially in new urban developments. However, quantitative data on building energy performance as a function of urban density, building compactness and orientation, building use and supply options are rarely available during the design of new cities or early scenario analysis for existing city quarters, making it difficult for cities to effectively evaluate which concepts work today and in the future. The paper proposes a methodology to assess the energy demand and supply options as a function of the availability of geometry, building standard and use data. An automated procedure was implemented to identify each building’s geometry and volume and transfer the information to a simulation tool, which then calculates heating demand and solar energy generation on roofs and facades. The simulation includes shading calculations for each segment of the façades and roofs and thus allows a very detailed quantification of the building energy demand. By applying the methodology to a case study city quarter designed in an urban competition in Munich, it could be shown how the urban design influences the energy demand of the quarter and which fractions of renewable energy can be integrated into the roofs. While the building insulation standard and use are the is most important criteria for building energy efficiency (with an impact of more than a factor 2), the exact geometrical form, compactness and urban shading effects influences the energy demand by 10–20%. On the other hand, the detailed roof geometry and orientation influences the possible solar coverage of electricity or thermal needs. Zero energy city quarters with solar resources alone are only possible when all available building surface areas are fully optimized and do not need to fulfill other requirements such as providing roof gardens, terraces or others. Combinations with other more centralized renewable resources such as deep geothermal, solar or biomass heat or cogeneration plants are often necessary to achieve zero energy balances. [ABSTRACT FROM AUTHOR]

Fuel Poverty is a big challenge for everybody: politicians, decision-makers, technicians, researchers, etc. In Italy, a strategy to solve fuel poverty involves action in order to reduce energy prices, the AEEG (Italian regulatory authority for electricity gas and water) has defined an Assist Tariff for poor people. Fuel poverty depends on family income and energy prices. Building energy performance also influences required energy consumption, and is a contributing cause of fuel poverty. Subsequently, it is possible to introduce a new Fuel Poverty risk Index, correlated to Building Energy Performance: the Building Fuel Poverty index. The Building Fuel Poverty Index (B FP ) allows us to quantify how many buildings need direct action and/or just economic incentives . The index should be adopted in order to identify subjects that can afford to pay building energy refurbishment. The paper proposes an index in order to evaluate fuel poverty condition correlated to building energy performance. [ABSTRACT FROM AUTHOR]

• Energy performance certificate (EPC) to c contribute in defining a energy poverty index. • Mapping building energy poverty risks to support policy actions for tackling energy poverty. • Merging GIS tool and EPC database to obtain city energy poverty maps at city (Bologna) level. Energy poverty is defined as the condition in which low-income people is no more able to face the costs of energy bills and consequently accept to live in cold and uncomfortable houses. In recent years the scientific literature about this specific issue has registered a significant growth, despite the problem is mainly approached from an econometric point of view to which instead medical, social and energy aspects have to be coupled. Recent EU policies like the Clean Energy Package and the Energy Building Performance Directive Recast III encourage to increase the efforts to tackle and eradicate the energy poverty. The paper approaches the problem focusing on one of its complementary causes: the building energy performance. The adopted methodology includes the calculation of the energy costs for each household, the definition of the energy poverty threshold, the calculation of the related building energy performance limit, otherwise leading to the energy poverty condition. These data, associated with the energy performance certificates, are used to create a GIS based mapping of the buildings potentially affected by energy poverty. Maps can be used to support decision making process in addressing appropriate strategies at urban level to tackle the energy poverty risk. The paper includes a case study in the city of Bologna where the proposed methodology is tested. [ABSTRACT FROM AUTHOR]

To assess a building energy performance, the consumption being predicted or estimated during the design stage is compared to the measured consumption when the building is operational. When valuing this performance, many buildings show significant differences between the calculated and measured consumption. In order to assess the performance accurately and ensure the thermal efficiency of the building, it is necessary to evaluate the uncertainties involved not only in measurement but also those induced by the propagation of the dynamic and the static input data in the model being used. The evaluation of measurement uncertainty is based on both the knowledge about the measurement process and the input quantities which influence the result of measurement. Measurement uncertainty can be evaluated within the framework of conventional statistics presented in the Guide to the Expression of Measurement Uncertainty (GUM) as well as by Bayesian Statistical Theory (BST). Another choice is the use of numerical methods like Monte Carlo Simulation (MCS). In this paper, we proposed to evaluate the uncertainty associated to the use of a simplified model for the estimation of the energy consumption of a given building. A detailed review and discussion of these three approaches (GUM, MCS and BST) is given. Therefore, an office building has been monitored and multiple temperature sensors have been mounted on candidate locations to get required data. The monitored zone is composed of six offices and has an overall surface of 102 m². [ABSTRACT FROM AUTHOR]

The use of efficient design parameters in the initial stages of the life cycle of a building project helps to reduce the final energy demand. This article presents research results on the relation between the morphology of a building and its energy efficiency. Three types of residential buildings are analyzed: the single-family detached house, semidetached house and multidwelling building. The cases studied modeled in EnergyPlus to obtain building energy consumption per useful built surface. Also considered is the energy produced, thanks to the installation of photovoltaic solar panels on the building roof and on 50 % of the south façade surface. The paper provides a method to obtain the curve that shows the difference between the energy demand of residential buildings for various uses (HVAC, lighting, etc.) and the energy generated by installed solar panels in the building. The results reveal that the single-family detached housing model is the less energy-efficient. In the case of multidwelling houses, the optimal building height is obtained to reduce building energy consumption depending on total useful built area. The results show that up to 25 % of multidwelling building energy demand can be satisfied by solar energy on the rooftop and the façade. The balance between the energy demand and energy production of the building highlights the dimensional parameters that define optimal building shape from an energy efficiency perspective. The results obtained can be usefully applied to estimate the optimal geometric characteristics for a building of the same total surface area, which maximally reduces the final energy demand. [ABSTRACT FROM AUTHOR]

Building Automation System (BAS) plays an important role in building operation nowadays. A huge amount of building operational data is stored in BAS; however, the data can seldom be effectively utilized due to the lack of powerful tools for analyzing the large data. Data mining (DM) is a promising technology for discovering knowledge hidden in large data. This paper presents a generic framework for knowledge discovery in massive BAS data using DM techniques. The framework is specifically designed considering the low quality and complexity of BAS data, the diversity of advanced DM techniques, as well as the integration of knowledge discovered by DM techniques and domain knowledge in the building field. The framework mainly consists of four phases, i.e., data exploration, data partitioning, knowledge discovery, and post-mining. The framework is applied to analyze the BAS data of the tallest building in Hong Kong. The analysis of variance (ANOVA) method is adopted to identify the most significant time variables to the aggregated power consumption. Then the clustering analysis is used to identify the typical operation patterns in terms of power consumption. Eight operation patterns have been identified and therefore the entire BAS data are partitioned into eight subsets. The quantitative association rule mining (QARM) method is adopted for knowledge discovery in each subset considering most of BAS data are numeric type. To enhance the efficiency of the post-mining phase, two indices are proposed for fast and conveniently identifying and utilizing potentially interesting rules discovered by QARM. The knowledge discovered is successfully used for understanding the building operating behaviors, identifying non-typical operating conditions and detecting faulty conditions. [ABSTRACT FROM AUTHOR]

Modeling of energy consumption in buildings is essential for different applications such as building energy management and establishing baselines. This makes building energy consumption estimation as a key tool to reduce energy consumption and emissions. Energy performance of building is complex, since it depends on several parameters related to the building characteristics, equipment and systems, weather, occupants, and sociological influences. This paper presents a new model to predict and quantify energy consumption in commercial buildings in the early stages of building design. Building simulation software including eQUEST and DOE-2 was used to build and simulate individual building configuration that were generated using Monte Carlo simulation techniques. Ten thousands simulations for seven building shapes were performed to create a comprehensive dataset covering the full ranges of design parameters. The present study considered building materials, their thickness, building shape, and occupant schedule as design variables since building energy performance is sensitive to these variables. Then, the results of the energy simulations were implemented into a set of regression equation to predict the energy consumption in each design scenario. A good agreement was seen between the predicted data based on the developed regression model and DOE simulation and the maximum error was less than 5%. It is envisioned that the developed regression models can be used to estimate the total energy consumption in the early stages of the design when different building schemes and design concepts are being considered. [ABSTRACT FROM AUTHOR]

Traditional approaches to understand the problem of the energy performance in the domestic sector include on-site surveys by energy assessors and the installation of complex home energy monitoring systems. The time and money that needs to be invested by the occupants and the form of feedback generated by these approaches often makes them unattractive to householders. This paper demonstrates a simple, low cost method that generates thermal diagnostics for dwellings, measuring only one field dataset; internal temperature over a period of 1 week. A thermal model, which is essentially a learning algorithm, generates a set of thermal diagnostics about the primary heating system, the occupants’ preferences and the impact of certain interventions, such as lowering the thermostat set-point. A simple clustering approach is also proposed to categorise homes according to their building fabric thermal performance and occupants’ energy efficiency with respect to ventilation. The advantage of this clustering approach is that the occupants receive tailored advice on certain actions that if taken will improve the overall thermal performance of a dwelling. Due to the method’s low cost and simplicity it could facilitate government initiatives, such as the ‘Green Deal’ in the UK. [ABSTRACT FROM AUTHOR]

• An uncertainty propagation method was developed which captures climate uncertainties. • The method is applicable to cases where few future weather files can be developed. • The method can reduce computation requirements following a probabilistic approach. • The impact of equipment modernization using updated design day files is investigated. In the United States, buildings consume a significant portion of total energy use and contribute to carbon emissions. Therefore, evaluating their performance for sustainable design is critical. While building standards are updated frequently to improve building efficiency, the climate is changing and classical (deterministic) approaches to quantifying building energy use may not be accurate. Significant uncertainties exist in current models of future building performance. Probabilistic approaches to evaluate whole building performance and account for climate uncertainties require large input data of climate projections with detailed spatial and temporal resolution and considerable computational resources. This paper offers a four-step process to propagate climate uncertainties from future climate projections into building energy performance. The climate uncertainty propagation method consists of a combination of regression, distribution fitting and random sampling. It has the potential to capture climate uncertainties in building simulation tools and is applicable to cases where limited future weather files can be produced. In addition, we include the use of updated design day files in future building performance analysis. Design days drive equipment modernization impacting whole building energy use under climate change. The Department of Energy office reference buildings for Philadelphia climate conditions are used as the case study. The aim of this research is to generate a probabilistic model that captures future building energy performance trends where can be applicable to regions that have limited future hourly weather files to be incorporated into building simulation tools. The results of this paper are intended to promote a probabilistic approach to account for climate uncertainties and to provide guidance in analyzing buildings projected energy consumption under climate change. [ABSTRACT FROM AUTHOR]

• An overall panorama of the school buildings in Brazil was presented. • Energy billed data and survey evidence were used to build a statistical stock model. • The Weibull distribution was the most adequate to represent the continuous variables. • kWh/student·year was more suitable than kWh/m2·year to model Brazilian school buildings stock. • Thermal performance is an issue to be greatly improved in schools in Brazil. Understanding the actual conditions in school buildings regarding energy usage and environmental satisfaction gives way to the development of consistent building stock modelling. In the Brazilian context, a deep-rooted inefficient structure of energy management implies a lack of mapping the actual conditions of the public schools. Therefore, this paper aimed to assess the actual energy performance of school buildings in Brazil. This paper innovates by integrating questionnaire responses and billed energy data to allow the construction of an evidence-based stock model through a statistical approach, which was never performed for the Brazilian building stock. Moreover, the association of energy use intensity (EUI) with the core aspects of building characteristics, occupancy, environmental satisfaction and necessary improvements mapped the relevance of such aspects in energy usage patterns. Additionally, a discussion of the EUI indicators was addressed. Results showed an unprecedented broad panorama of the school building stock in Brazil and its main features. In terms of statistical analysis, the Weibull distribution was appropriate for representing the continuous variables of the stock. The EUI as a function of the number of students was more suitable than the floor-plan area to achieve a reliable energy performance analysis. Several tests were performed to assess if the energy management, environmental satisfaction and maintenance practices impacted the EUI or not. Finally, the main conclusion was that the stock model obtained was simple and fit the energy management structure in the Brazilian scenario. [ABSTRACT FROM AUTHOR]

Despite the multiple advantages of prefabricated compared to conventional buildings, such as significant reductions in cost and time, improved quality and accuracy in manufacture, easy dismantling and reuse of components, reduction in environmental degradation, increase of productivity gains, etc., they still share a small part of the European building stock, mainly in the Mediterranean. This paper attempts to highlight the potential of prefabricated buildings to achieve advanced levels of performance, particularly as regards their thermal and energy behavior. More specifically, in this paper the energy needs of a single-family building constructed with prefabricated elements is analyzed, considering different climate contexts. The prefabricated elements comprising the building envelope were developed in order to address specific requirements with respect to their structural, hygrothermal, energy, fire, acoustical, and environmental performance, within the research project SUPRIM (sustainable preconstructed innovative module). The new multifunctional building element, also incorporating phase change materials for increased latent thermal heat storage, has been proven to be beneficial in all the examined climate zones. The results of the relevant studies will highlight the contribution of the new prefabricated element to the sustainability of the overall construction, as well as its advantages when compared with conventional constructions. [ABSTRACT FROM AUTHOR]

• The aim of the paper is to predict the hourly thermal performance data by using Artificial Neural Network based on short-term measured data. • The paper discusses the existing applications and propose a new method to provide an accurate result. • The paper is developed through a case study; however, the method could be applicable for any building or location. • The importance of this study comes from the ability of the predicting the whole heating seasons data to be used in a real-time calibration process.A thermal dataset were composed by validating the result of the prediction with measured data, which saved about 77% of the measurement's time. This study aims to predict the hourly thermal performance data during the heating season based on short-term measured data by comparing the results with the simulated data for a big scale residential building. The measurement period was done for about 31.5 days during the heating season. Alternatively, the heating season was evaluated with the simulated heating consumption data as the period from November 15th to March 31st (137 days) in order to predict the data for inside temperature and relative humidity. Relatedly, Artificial Neural Network (ANN) was designed for the prediction model. Outside dry-bulb temperature, outside dew-point temperature, wind speed, wind direction, atmospheric pressure, solar azimuth, and heating consumption were set as independent variables (inputs) along with temperature and humidity as targeted variables of the model. Four FeedForword – BackPropagation of ANNs were used as a network for each measurement point inside of the building where each ANN includes three layers, defined as input layers, hidden layer, and output layer. A thermal dataset for the heating season was composed by validating the result of the prediction with measured data, which saved about 77% of the heating season measurement's time. Then, by comparing composed dataset with the simulated thermal data, it was obtained that the heating system is performing well and close to the expectations. The approached prediction work provides the possibility to apply real-time calibration with the monitoring system which was already implemented in the building. Therefore, the produced dataset would ensure the quality of the real-time measured data for any unexpected condition during the building operation. as well as the accuracy of simulated data for any unforeseen inputs. Results show that the ANN model might be used effectively to provide useful predictions for the indoor thermal data not only to cover the missing measured data, but also to support the calibration process, the monitoring system and validating the simulation set points. [ABSTRACT FROM AUTHOR]

Climate change, driven by greenhouse gas emissions, is a growing global concern, threatening world-wide environment, health and economy. Energy needs for buildings are a large source of greenhouse gas emissions. As the energy needs of buildings strongly depends on weather patterns, this paper investigates how climate change may impact building heating and cooling loads, cost-optimal efficiency measures, and renewable energy production. Eight locations (Stockholm, Milan, Vienna, Madrid, Paris, Munich, Lisbon, and Rome) highlight differences among European climates. Weather datasets, commonly used in building energy simulations, are evaluated to see how climatic parameters have changed over recent decades. A future climate change scenario (with uncertainties) is analyzed for the year 2060. Weather files are used to drive building energy simulations for a standard baseline and a (Nearly Zero Energy Building) NZEB residential building whose design is improved using a cost-optimization approach. The analysis indicates most currently available weather datasets cannot assure reliable results with building simulations. We find the energy balance in European buildings will significantly change under future conditions: heating will decrease by 38%–57%, while cooling will increase by +99%–380% depending on location. In future NZEBs, efficiency measures to reduce cooling needs and overheating will be favored (e.g. roof insulation, window type, solar shading, envelope finishes), illustrating how improving energy efficiency will be more crucial within climate change scenarios. Compared to the baseline, more efficient NZEBs will enable renewable energy to much better cover building needs. There will also be advantages from reducing winter and summer peak demand, particularly when coupled to short-term electrical storage. When solar resource is limited in winter, more airtight, better-insulated NZEBs improve PV self-consumption. • Climate change impact is studied in baseline and NZEBs in different climates. • Different weather datasets and a climate change scenario are simulated. • Future heating loads decrease by 38%–57%, cooling increase by +99%–380%. • Energy efficiency measures to reduce cooling and overheating are favored. • NZEBs enable renewable energy to much better cover building needs. [ABSTRACT FROM AUTHOR]

Earth-to-air heat exchangers (EAHEs) can be used in the ventilation systems of various types of buildings. Multipipe structures can be found in large-volume buildings, yet scientific analysis of such systems is rare. Annual energy gains and electricity consumption for equivalent single-pipe and multipipe systems are typically not available. This paper bridges this gap, presenting the results of experimental studies on pressure losses in three-, five- and seven-pipe EAHEs and analysis for the annual energy gains and electric energy consumption as compared to a single-pipe exchanger. The results showed that the multipipe EAHE can be successfully replaced by a single-pipe structure with the same thermal performance and similar pressure losses if a tube with the appropriate diameter is used. However, multipipe heat exchangers can also use pipes of larger diameter (manifolds and/or branches), which improves their energy efficiency and may then make them more advantageous than single-pipe structures. From this reason, ultimately, the final selection of exchanger geometry should take into account economic and environmental issues and also user preferences and their importance in the hierarchy. [ABSTRACT FROM AUTHOR]