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A prerequisite for decreasing the intensification of energy in buildings is to evaluate and understand the influencing factors of building energy performance (BEP). These factors include building envelope features and outdoor climactic conditions, among others. Based on the importance of the influencing factors in the development of the building energy prediction model, various researchers are continuously employing different types of factors based on their popularity in academic literature, without a proper investigation of the most relevant factors, which, in some cases, potentially leads to poor model performance. However, this can be due to the absence of an adequate comprehensive analysis or review of all factors influencing BEP ubiquitously. Therefore, this paper conducts a holistic and comprehensive review of studies that have explored the various factors influencing energy use in residential and commercial buildings. In total, 74 research articles were systematically selected from the Scopus, ScienceDirect, and Institute of Electrical Electronics Engineers (IEEE) databases. Subsequently, by means of a systematic and bibliometric analysis, this paper comprehensively analyzed several important factors influencing BEP. The results reveals the important factors (such as windows and roofs) and engendered or shed light on the application of some energy-efficient strategies such as the utilization of a green roof and photovoltaic (PV) window, among others. [ABSTRACT FROM AUTHOR]

The thermal properties of a building envelope are key indicators of the energy performance of the building. Therefore, methods are needed to determine quantities like the thermal transmittance (U-value) or heat capacitance in a fast, reliable way and with as little impact on the use of the building as possible. In this paper a technique is proposed that relies on a simplified electrical analogical model of building envelope components which can cover their dynamic thermal behavior. The parameters of this model are optimized to produce the best fit between simulated and measured outside surface temperatures. As the temperatures can be measured remotely with an infrared camera this approach requires significantly less installation effort and intrusion in the building than other methods. At the same time, a single measurement provides data for a large range of locations on a facade or a roof. The paper describes the method and a first experimental implementation of it. The experiment indicates that this method has the potential to produce results which have an accuracy that is comparable to standardized reference methods. [ABSTRACT FROM AUTHOR]

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]

Enhancing energy efficiency in buildings plays a pivotal role in realizing the ambitious objective of achieving carbon neutrality by 2050, as outlined in the European Green Deal. Roofs represent the technical element most affected by energy phenomena related to heat transfer: in winter, roofing can lose up to 35% of heat, and the summer heat flux can even be higher. This paper provides a catalogue of optimized and sustainable solutions, with a specific focus on standardization and prefabrication principles, for enhancing the energy efficiency of the most prevalent types of roofs that characterize the national residential building heritage. The methodological approach that guided the research presented in this article was based on the identification and study of the most common roofings in the diverse national residential building heritage, followed by their classification according to their construction era. In the context of essential energy retrofitting of deteriorated residential building stock, 21 optimized standardized solutions have been identified. The outcome of performance evaluations of the proposed solutions allowed the implementation of a matrix that can be a valuable support for designers in selecting the most efficient precalculated and prefabricated solutions for the national residential building heritage based on energy performance and sustainability criteria. [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 need to improve the sustainability of intensive livestock farming has led to an increasing adoption of Building Energy Simulation (BES) models for livestock houses. However, a consolidated body of knowledge specifically dedicated to these models is lacking in literature. This gap represents a significant obstacle to their widespread application and scalability in research and industry. The aim of this work is to pave the way for scaling the adoption of BES models for livestock houses by providing a comprehensive analysis of their application, development, and validation. For this aim, a systematic review of 42 papers—selected from over 795 results from the initial database query—is carried out. The findings underscored a growing body of research that involves BES models for different purposes. However, a common approach in both model development and validation is still lacking. This issue could hinder their scalability as a standard practice, especially in industry, also considering the limitations of BES models highlighted in this work. This review could represent a solid background for future research since provides an up-to-date framework on BES models for livestock houses and identifies future research opportunities. Moreover, it contributes to increasing the reliability of BES models for livestock houses by providing some recommendations for their validation. [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]

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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]

In this paper, we present the final results from the research project "Urban Abacus of Building Energy Performances (Abaco Urbano Energeticodegli Edifci–AUREE)" aimed at supporting the renovation process and energy efficiency enhancement of urban building stocks. The crux of the AUREE project is a Web–GIS GeoBlog portal with customized semantic dashboards aimed at sharing information on an urban built environment and promoting the participation of local stakeholders in its improvement. As the latest development of this research, a workflow that integrates the AUREE portal with BIM authoring and an open-source IoT platform is implemented and applied to an experimental case study concerning a public building in Carbonia (Italy). The headquarters of the Sotacarbo Sustainable Energy Research Center was selected as the case study. The presented results proved that it was possible to create a valid open system, which was accessible to both specialist and unskilled users, and aimed at guiding, through a progressive knowledge deepening, common end-users toward proper conscious "energy behaviors" as well as public administrations and decision-makers toward sustainable facility management. Later, the proposed open system could also be suitable to be used as an effective tool to support the rising "energy communities". [ABSTRACT FROM AUTHOR]

New and refurbished non-domestic buildings are failing to live up to their anticipated performance. Shortfalls show in excess energy consumption, high carbon dioxide emissions and other failings in quantitative and qualitative performance metrics. This paper describes the component parts of the performance gap using evidence from building performance evaluations. It introduces a way of visualising the consequences of decisions and actions that are known to compromise performance outcomes using a performance curve methodology (the S-curve) which plots performance, and the root causes of underperformance, from project inception to initial operation and beyond. The paper tests the hypothesis with two case studies. It also covers the initial development of a prototype visualisation tool designed to enable live projects to track emerging operational energy and emissions against a high energy and emissions trajectory created from empirical evidence. The tool aims to help practitioners identify key risk factors that could compromise building performance and mitigate these risks at different stages of procurement. Practical application: The Operational Energy and Carbon (OpEC) visualisation tool is designed for wide industrial application, on all sizes of a non-domestic building project, large and small. It aims to visualise the likely outturn energy performance of a project by calculating the penalties for shortcomings in project delivery. The penalties are visualised as weighted trajectories of energy and carbon dioxide emissions. The prototype tool aims to fill a gap between the capabilities of powerful energy modelling tools used in design and the capacity of non-specialist stakeholders to understand the emerging energy characteristics of a project as it moves through procurement, design, construction, and delivery. [ABSTRACT FROM AUTHOR]

Occupant behavior controls a building's energy system to adapt the indoor environment, significantly increasing building energy consumption. Occupant behavior, which refers to the occupancy inside a building and their interaction with building systems (windows, blinds, thermostats, lighting and appliances, etc.), has been largely overlooked in building energy performance analysis. These factors make it essential to design sustainable buildings. It is widely acknowledged in the literature that there is an alarming performance gap between the estimated and actual energy consumption in buildings. This paper proposes a systematic literature review on energy-related occupant behaviors and their implications for energy performance. It aims to better understand occupant behavior, existing behavior modeling approaches and their limitations, and key influential parameters on building energy performance. It is based on a survey of ScienceDirect, Web of science and Scopus scientific databases, using their bibliometric analysis tools together with the VOSviewer software. Finally, this study identifies the following significant research gaps for future development: limitations of the generic and robust occupant behavior model; lack of actual data for validation; lack of research on different types of buildings (institutions, university buildings); limitations of considering all factors which influence occupant behavior; missing the detailed realistic situations of occupant behavior; integrating building information modeling (BIM) into building energy modeling. [ABSTRACT FROM AUTHOR]

To date, energy consumption in buildings accounts for a significant part of the total amount of energy consumed worldwide. The effect of ageing and degradation of various building components is one of the least studied reasons for the possible increase in energy consumed in buildings over time. In addition, there is a clear lack of practical guidelines that would help specialists take this factor into account. In this paper, an attempt is made to assess a possible change in the energy performance of buildings due to the degradation of their various components (insulated glass units, thermal insulation, airtightness, solar reflectivity of the building envelope, and photovoltaic modules). Detached and apartment buildings in hot-humid climates with reference to the United Arab Emirates (UAE) were considered. The study was based on simulation research using EnergyPlus, in which the initially collected data on the possible deterioration of the properties of various building components was used for dynamic thermal simulation of selected buildings. The results showed an increase in energy consumption for cooling in detached houses might reach up to 9.53–38.4% over 25 years for more airtight and insulated buildings and 12.28–34.93% for less airtight and insulated buildings. As a result, certain patterns of changes in energy consumption for cooling buildings were established, based on which a set of guidelines was developed. These guidelines can help specialists in various fields better understand the trends in the energy performance of buildings under the influence of degradation processes and take appropriate measures. [ABSTRACT FROM AUTHOR]

The energy performance prediction of buildings plays a significant role in the design phases. Theoretical analysis and statistical analysis are typically carried out to predict energy consumption. However, due to the complexity of the building characteristics, precise energy performance can hardly be predicted in the early design stage. This study considers both building information modeling (BIM) and statistical approaches, including several regression models for the prediction purpose. This paper also highlights a number of findings of energy modeling related to building energy performance simulation software, particularly Autodesk Green Building Studio. In this research, the geometric models were created using Autodesk Revit. Based on the energy simulation conducted by Autodesk Green Building Studio (GBS), the energy properties of five prototype and case study models were determined. The GBS simulation was carried out using DOE 2.2 engine. Eight parameters were used in BIM, including building type, location, building area, analysis year, floor-to-ceiling height, floor construction, wall construction, and ceiling construction. The Monte Carlo simulation method was performed to predict precise energy consumption. Among the regression models developed, the single variable linear regression models appear to have high accuracy. Although there exist some limitations in applying the equation in EUI prediction, the rough estimation of energy use was realized. Regression model validation was carried out using the model from the case study and Monte Carlo simulation results. A total of 35 runs of validation were performed, and most differences were maintained within 5%. The results show some limitations in the application of the linear regression model. [ABSTRACT FROM AUTHOR]

Buildings account for a large portion of the total energy use in the US; therefore, improving the operation of typical variable-air-volume (VAV) systems in buildings can provide a tremendous economic opportunity. ASHRAE Guideline 36 recommends a resetting strategy for supply air temperature (SAT) for VAV systems based on outside air temperature. However, this strategy may not produce optimal performance, particularly when simultaneous cooling and heating occurs in zones. In addition, there is no strategy recommended in the Guideline to reset the zone minimum airflow set point in a single-duct VAV terminal unit with reheat, although this setpoint has a great impact on zone reheat requirements and ventilation efficiency. Thus, this paper introduces new strategies to reset both the SAT and zone minimum airflow rate set points to improve the efficiency of typical VAV systems. The strategies were tested under various conditions through experiments performed in fully instrumented VAV systems located in the HVAC lab at the University of Cincinnati. The experiments were conducted on a chilled-water VAV system that serves three controlled zones with hot-water reheat VAV boxes controlled by a typical commercial BACnet web-based building automation system BAS. The simulation studies were performed using the building energy simulation software EnergyPlus to evaluate the strategies at a larger scale in various locations. The simulation results show that the proposed resetting strategies can provide fan energy savings between 1.6% and 5.7% and heating load savings between 7.7% to 33.7%, depending on the location. The laboratory testing shows that the proposed strategies can provide stable control performance in actual systems as well as achieving the anticipated reheat and fan energy savings. The result offers significant improvements that can be implemented in the Guideline for single-duct VAV system operation and control. [ABSTRACT FROM AUTHOR]

A rise in the number of EVs (electric vehicles) in Europe is putting pressure on power grids. At an urban scale, Positive Energy Districts (PEDs) are devised as archetypes of (small) urban districts managing a set of interconnected buildings and district elements (lighting system, vehicles, smart grid, etc.). This paper offers a comprehensive analysis of the impact of e-mobility in a PED, simulated using MATLAB-Simulink software. The PED, a small district in northern Spain, is assessed in five scenarios representing varying requirements in terms of energy efficiency of buildings, type of street lighting and number of EVs. The results suggest that the energy rating of the buildings (ranging from A for the most efficient to E) conditions the annual energy balance. A PED with six interconnected buildings (3 residential and 3 of public use) and 405 EVs (as a baseline) only achieves positivity when the buildings have a high energy rating (certificate A or B). In the most efficient case (A-rated buildings), simulation results show that the PED can support 695 EVs; in other words, it can provide nearly 9 million green kilometres. This result represents a potential 71% saving in carbon emissions from e-mobility alone (as compared to the use of fossil-fuel vehicles), thus contributing a reduction in the carbon footprint of the district and the city as a whole. [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]

The thermal properties of a building envelope are key indicators of the energy performance of the building. Therefore, methods are needed to determine quantities like the thermal transmittance (U-value) or heat capacitance in a fast, reliable way and with as little impact on the use of the building as possible. In this paper a technique is proposed that relies on a simplified electrical analogical model of building envelope components which can cover their dynamic thermal behavior. The parameters of this model are optimized to produce the best fit between simulated and measured outside surface temperatures. As the temperatures can be measured remotely with an infrared camera this approach requires significantly less installation effort and intrusion in the building than other methods. At the same time, a single measurement provides data for a large range of locations on a facade or a roof. The paper describes the method and a first experimental implementation of it. The experiment indicates that this method has the potential to produce results which have an accuracy that is comparable to standardized reference methods.

Performance-based climatic zoning for building energy efficiency applications has shown significant advantages when compared to methods that only rely on climate data. However, previous studies have primarily focused on small regions, which raises questions about the effectiveness of this approach for larger countries. To address this gap, this paper presents the first application of performance-based zoning to Brazil, detailing the process of revising the Brazilian climatic zoning standard using an open-source toolbox. Results of simulations for 100 variants of a detached dwelling using climatic data for 298 locations were clustered and analysed in terms of energy demand, thermal comfort, and mould growth risk (not previously used in climatic zoning studies). The paper employed a methodical approach to incorporate expert insights with the aim of enhancing and validating the outputs of the climatic zoning. To compare the proposed method with the current zoning of Brazil, and the degree-days zoning, the mean percentage of potentially misclassified areas (MPMA) was used, and its calculation was improved to handle an arbitrary number of conflicting performance indicators. The involvement of local experts was crucial in developing the proposed zoning method as they played a key role in defining the number of zones and establishing zones boundaries. The feedback from local experts confirmed the proposed method as a significant improvement over the current zoning, which is supported by the MPMA results (14%), outperforming both the current Brazilian and degree-day zones. The zoning method is applicable to countries of any size and level of complexity. • Performance-based zoning was successfully applied in the zoning of a large country. • Performance-based zoning considers all climatic variables simultaneously, addressing their complex interrelationship. • Performance-based zoning correctly captures the impact of topography and continentality in the climate. • The use of indoor mould growth risk improves the differentiation between climates with low and high humidity. • Expert panels provide valuable input in the definition of zone boundaries where climate data is not available. [ABSTRACT FROM AUTHOR]

Bifacial photovoltaic sunshade (BiPVS) is an innovative building-integrated photovoltaic (BIPV) technology. Vertically mounted BiPVS is capable of converting part of the incident solar radiation into electricity, regulating the indoor heat gain from solar penetration and improving daylighting. An excellent BiPVS design should comprehensively consider its impact on building performance and economic viability. This study aims to address this issue by proposing a parametric design-based multi-objective optimization (MOO) framework to maximize indoor useful daylight illuminance, minimize air-conditioning energy consumption, and shorten the payback period by optimizing BiPVS design parameters. The framework utilizes the Ladybug, Honeybee, and Wallacei plugins on the Rhino-Grasshopper simulation platform. It validates the optimization potential of BiPVS in a typical office located in a hot summer and warm winter zone. The results indicate that BiPVS has significant energy-saving and daylighting potential. Compared to the baseline model without BiPVS, useful daylight illuminance is increased by 39.44%, air-conditioning energy consumption is reduced by 12.61%, and the economically satisfactory payback period is 4.80 years. This study provides a practical solution for the competing objectives of daylighting and energy saving in buildings with significant renewable energy utilization. The developed framework is highly efficient and versatile and can be applied to other BIPV designs, which benefits the realization of carbon-neutral goals in the building sector. [ABSTRACT FROM AUTHOR]

Lack of effective collaboration among energy-related stakeholders is considered as a critical factor that affects building energy performance. Although previous studies have explored energy-related stakeholders' collaboration behavior, there is still a lack of simulation model that can be easily used for collaboration analysis with the consideration of dynamic nature, generality and practicability. To fill this gap, a hybrid dynamic model with the combination of system dynamic and agent-based modeling was developed in this study. Strict model structure tests and model behavior tests were conducted to validate the proposed model. To demonstrate the use of the hybrid model, a tentative exploration of the collaboration among stakeholders was presented. The simulation results showed that (1) a single policy of government subsidies has some limitations for promoting collaboration and improving building energy performance; (2) technical innovation can improve not only the rate but also the quality of collaboration; (3) the effect of long-term gain on collaboration and the building energy performance is sustained; (4) the penalties has an upper limit to reduce the building energy performance gap; (5) the integrated effect on the building energy performance may be less significant than that of the simple sum of each strategy; and (6) there is an incremental and then decreasing marginal utility of integrated strategies. The hybrid model provides quantitative research methods to better analyze the collaboration among energy-related stakeholders. Both the dynamic collaboration decision process and the bounded rationality of stakeholders have been considered. [ABSTRACT FROM AUTHOR]

In response to escalating energy demands and global warming concerns, the Passive House Standard has emerged as a solution in residential construction, aiming to drastically reduce energy consumption and operational costs primarily through high-performance building envelopes. While a considerable volume of the literature has focused on the Passivhaus Institute (PHI) standards, predominantly in European contexts, there is a gap in research on the Passive House Institute US (Phius) standards, particularly in North American climates. This study conducts a quantitative comparative analysis of two adjacent multifamily residential buildings in Central Pennsylvania, Climate Zone 5A—one built using conventional construction methods and the other following Passive House (PHIUS+ 2015) certification standards—to validate the energy efficiency improvements attributed to Passive House designs. A comparative analysis of the whole building energy use over two years reveals that the Passive House building consumes approximately 50% less energy than its conventional counterpart in terms of whole building energy use and the national median recommended benchmark metric defined by the Energy Star Portfolio Manager. These findings emphasize the potential for significant energy savings and greenhouse gas reductions in residential buildings, highlighting the necessity for policymakers and governments to incentivize the adoption of Passive House standards to achieve environmental sustainability and reduce energy costs for society. [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]

Operational building energy consumption accounts for 55% of global energy consumption. Most of this is attributed to residential buildings, as they make up the largest building type when compared to the total building stock worldwide. As the building envelope is a major contributor to building energy performance, especially the external walls, its optimisation is therefore imperative to reduce energy consumption and carbon emissions. This study set out to assess the effects of waste material additions to external walls and their effect on building energy performance. This research aimed to critically investigate the effect of rice husk ash (RHA) masonry blocks on building energy performance when compared to conventional masonry blocks in tropical climates. A mix of methods, including experimental investigation and simulation studies, were employed for this study. Three variations of RHA block samples were created for this investigation: RHA 5%, RHA 10%, and RHA 15%. Using prototype buildings from the study context, the building simulation results helped quantify the impact on building energy performance from the reuse of rice waste. The largest improvement to the building fabric was recorded with the RHA15% blocks, which resulted in a 9.9% and 11.3% reduction in solar heat gains through the external walls for the selected bungalow and duplex/storey building, respectively. This resulted in a 6.55% and 4.2% reduction in cooling loads and a 4.1% and 2.8% reduction in carbon emissions, respectively, for the bungalow and duplex/storey building. The findings of this research will prove valuable to householders, researchers, architects, and policymakers in their decision-making processes. The findings will also be useful in introducing new methods that can be adopted for similar studies, bridging the knowledge gap while promoting a circular economy through the reuse of landfilled waste. [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]

[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]

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.

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]

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]

Buildings' contribution to global final energy use is about 30%, which makes them a primary focus for implementing energy-efficient measures. Building energy efficiency is an important consideration for residential buildings due to the significant environmental impact of energy consumption and the rising cost of energy. Estimating and optimizing a building's energy performance is an efficient method to reduce its environmental impact and cost. There exists a lack of accuracy in estimating the energy performance of a building due to approximations in the monitored data as well as a lack of consideration for occupants' energy use behavior. This study aimed to develop a comprehensive framework that assists in accurately estimating building energy performance considering occupants' energy use behavior. The framework proposed a scheme to collect occupant behavior data, such as occupancy patterns, appliance usage, and lighting conditions, through a living-lab setup and developing an occupants' behavior model that was utilized for more accurate building energy modeling and performance analysis. [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]

The paper presents an analysis of the application of an air-to-water electric compressor heat pump (AWHP) for the recovery of waste heat from the exhaust air in a typical multifamily residential building and the use of this heat for space heating, as well as the impact of this solution on the building energy performance (the PPR index). Simulations were performed in TRNSYS for five locations in Poland (Koszalin, Wrocław, Lublin, Białystok, Suwałki), for various heating system parameters (80/60 °C, 75/65 °C, 70/50 °C, 55/45 °C, 35/28 °C), for various temperature limitations of heat pump operation. It was shown that the analyzed system has great potential from an energy and environmental point of view. It can provide significant benefits in terms of the energy performance of the building, depending on the system parameters. The results show that the most energy-efficient system is the one with the lowest heating system temperatures. Moreover, implementing a temperature limitation on the heat pump operation improves its efficiency, but the higher the design parameters of the heating installation and the lower the limitation, the lower the heat pump contribution, and the higher the SCOP and the PPR. The energy effect is also influenced by location, but its scale depends on the parameters of the heating system and the temperature limitation of the heat pump's operation. It is more significant for lower heating system parameters. This system enables the possibility of further reducing the demand for nonrenewable primary energy by powering the heat pump with photovoltaic cells. [ABSTRACT FROM AUTHOR]

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.

Buildings and the mobility sectors are the two sectors that currently utilize large amount of fossil-based energy. The aim of the paper is to, critically analyse the integration of electric vehicles (EV) energy load with the building's energy load. The qualitative and quantitative methods are used to analyse the nearly/net zero energy buildings and the mobility plans of the Europe along with the challenges of the plans. It is proposed to either include or exclude the EV load within the building's energy load and follow the emissions calculation path, rather than energy calculation path for buildings to identify the benefits. Two real case studies in a central European climate are used to analysis the energy performance of the building with and without EV load integration and the emissions produced due to their interaction. It is shown that by replacing fossil-fuel cars with EVs within the building boundary, overall emissions can be reduced by 11–35% depending on the case study. However, the energy demand increased by 27–95% when the EV load was added with the building load. Hence, the goal to reach the nearly/net zero energy building target becomes more challenging. Therefore, the emission path can present the benefits of EV and building load integration. [ABSTRACT FROM AUTHOR]

This paper presents basic principles of built-environment physics' modelling, and it reviews common computational tools and capabilities in a scope of practical design approaches for retrofitting purposes. Well-established simulation models and methods, with applications found mainly in the international scientific literature, are described by means of strengths and weaknesses as regards related tools' availability, easiness to use, and reliability towards the determination of the optimal blends of retrofit measures for building energy upgrading and Urban Heat Island (UHI) mitigation. The various characteristics of computational approaches are listed and collated by means of comparison among the principal modelling methods as well as among the respective computational tools that may be used for simulation and decision-making purposes. Insights of coupling between building energy and urban microclimate models are also presented. The main goal was to provide a comprehensive overview of available simulation methods that can be used at the early design stages for planning retrofitting strategies and guiding engineers and technical professionals through the simulation tools' options oriented to the considered case study. [ABSTRACT FROM AUTHOR]

As building systems account for almost half of the total energy consumed by the building sector to provide space heating, cooling, and ventilation, efficiently designing these systems can be the key to energy conservation in buildings. Dual VAV systems with an effective control strategy can substantially reduce the energy consumption in buildings, providing a significant scope of further research on this system configuration. This paper proposes to utilize the warm air duct of the dual VAV system as a dedicated outdoor air (DOA) unit when no heating is required, which allows the cooling load to be effectively distributed between two ducts. A specific control sequence is proposed with different supply air temperature reset strategies to estimate the heating, cooling loads, and fan power energy consumption of the proposed system. A simple two-zone office building is taken as a preliminary case study to simulate the airflow rates and fan power of a single duct VAV and proposed dual VAV systems to illustrate the concept. Finally, a larger multi-zone office building is simulated to measure the annual heating, cooling loads, and fan power energy and compare the energy savings among the systems. The results show significant fan power reduction ranging from 1.7 to 9% and notable heating energy reduction up to 76.5% with a small amount of cooling load reduction varying from 0.76 to 2.56% depending on the different locations for the proposed dual VAV systems. Further energy savings from different supply air temperature reset strategies demonstrate the opportunity of employing them according to climates and case studies. The proposed dual VAV system proves to have the potential to be adapted in buildings for the purpose of sustainability and energy savings. [ABSTRACT FROM AUTHOR]

Residential buildings in the Kingdom of Saudi Arabia (KSA) contribute to nearly half of the overall electricity consumption in the building stock, highlighting their significant role in energy consumption. While an upgraded energy code has been established and enforced for new buildings, existing buildings continue to operate at the same level of energy consumption. Therefore, there is a need for further energy upgrades in existing buildings. This study evaluates the energy savings potential of various energy retrofitting measures for a case study in Jeddah, KSA. Data from previous studies and current practices were collected and analyzed. Different energy upgrade measures, such as windows replacement, wall insulation upgrade, roof insulation upgrade, and air conditioning unit replacement, were selected and evaluated using a digital simulation tool called Design-Builder. The simulation results were compared to understand the potential percentage of energy savings. The average annual energy consumption (AAEC) was used as the primary performance indicator to compare the energy savings among the scenarios. The results demonstrate significant reductions in energy consumption for the proposed scenarios. Furthermore, the study examined the significant impact of uncertainties, specifically, the infiltration rate and AC setback temperature, on AAEC. In conclusion, the proposed scenarios have the potential to achieve substantial energy savings, ranging from 25% to 66%, depending on the number of energy retrofitting interventions employed. The findings of this study can serve as a useful reference for similar energy retrofitting projects. [ABSTRACT FROM AUTHOR]

High-performance insulation and airtight building envelopes are the two most important factors to optimize the building energy efficiency and thermal performance. However, thermal performance of building envelopes can be significantly affected by thermal bridging. Thermal bridges can take place at different locations of the building envelope, causing added transmission losses, increasing heat flow, lower inner surface temperatures. Thermal bridges are regarded in all national regulations for new buildings of all states of European Union (EU) but there are no specifications assessing balconies to wall junctions and the impact on the building thermal performance is not well regulated. In this paper, balcony to wall thermal bridge assessment using infrared thermography is highlighted. The originality is that, infrared thermography, has been applied to a real living latest building in the north east of France showing that there is noncompliance in the national regulations as some exigencies are not met. Thermography of thermal break windows and window doors has also revealed air leakage. The goal, is to illustrate that thermal imaging is a means of quality assurance to new building specifications and should include a clause specifying quality assurance procedure for balconies junction in national building regulations for all states of the EU.

The need to improve the sustainability of intensive livestock farming has led to an increasing adoption of Building Energy Simulation (BES) models for livestock houses. However, a consolidated body of knowledge specifically dedicated to these models is lacking in literature. This gap represents a significant obstacle to their widespread application and scalability in research and industry. The aim of this work is to pave the way for scaling the adoption of BES models for livestock houses by providing a comprehensive analysis of their application, development, and validation. For this aim, a systematic review of 42 papers—selected from over 795 results from the initial database query—is carried out. The findings underscored a growing body of research that involves BES models for different purposes. However, a common approach in both model development and validation is still lacking. This issue could hinder their scalability as a standard practice, especially in industry, also considering the limitations of BES models highlighted in this work. This review could represent a solid background for future research since provides an up-to-date framework on BES models for livestock houses and identifies future research opportunities. Moreover, it contributes to increasing the reliability of BES models for livestock houses by providing some recommendations for their validation.

The issues of reducing energy consumption in buildings and their decarbonisation are currently among the most pressing. However, such an important aspect of the problem under discussion as the impact of unavoidable degradation processes on energy demand in buildings remains poorly understood. In addition, there are only a limited number of practical guidelines that can be used to take this factor into account at the design stage and during the further operation of buildings. The aim of this work was to assess the potential impact of component degradation and ageing on heating energy consumption in buildings, including insulated glass units, thermal insulation, airtightness, heat recovery of mechanical ventilation systems, and photovoltaic modules. The detached and apartment buildings were considered to be in a cold climate in the context of the Republic of Belarus. The study was based on simulation research using EnergyPlus. As a result, it was found that a possible increase in heating energy consumption might reach 17.6–61.2% over 25 years in detached houses and up to 23.6–89.8% in apartment buildings. These indicators turned out to be higher than the previously identified values for cooling energy consumption in a hot–humid climate. Based on the findings, recommendations for considering the degradation factor in cold climates in practice were developed, which were compared and integrated into the author's existing guidelines. [ABSTRACT FROM AUTHOR]

Recently, metaheuristic algorithms have been recognized as applicable techniques for solving various computational complexities in energy problems. In this work, a powerful metaheuristic technique called the water cycle algorithm (WCA) is assessed for analyzing and predicting two annual parameters, namely thermal energy demand (TDA) and weighted average discomfort degree-hours (DDA), for a residential building. For this purpose, a double-target multi-layer perceptron (2TMLP) model is created to establish the connections between the TDA and DDA with the geometry and architecture of the building. These connections are then processed and optimized by the WCA using 80% of the data. Next, the applicability of the model is examined using the residual 20%. According to the results, the goodness-of-fit for the TDA and DDA was 98.67% and 99.74%, respectively, in terms of the Pearson correlation index. Moreover, a comparison between WCA-2TMLP and other hybrid models revealed that this model enjoys the highest accuracy of prediction. However, the shuffled complex evolution (SCE) optimizer has a better convergence rate. Hence, the final mathematical equation of the SCE-2TMLP is derived for directly predicting the TDA and DDA without the need of using programming environments. Altogether, this study may shed light on the applications of artificial intelligence for optimizing building energy performance and related components (e.g., heating, ventilation, and air conditioning systems) in new construction projects. [ABSTRACT FROM AUTHOR]