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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]

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]

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]

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]

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.

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.