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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]

Operational parameters of air handling units (AHUs) play an important role in the energy and comfort performance of commercial buildings. Current guidelines to determine these parameters are based on heuristics and are not informed by formal optimization. This paper presents a building performance optimization method to derive sequence of operations for multi-zone AHUs. To this end, 27 variants of a generic EnergyPlus office building model are built representing three levels of occupancy, envelope, and HVAC capacity scenarios. The supply temperature, morning start time, and economizer settings of the model are optimized for the 27 scenarios by using a genetic algorithm. The results highlight that optimal supply temperature setpoints transition from ∼12°C to a range of ∼16°C to ∼20°C over an outdoor temperature range of ∼20°C to ∼0°C. Energy savings are estimated as ∼20% relative to a reference case with a constant supply temperature and preheating/precooling period. [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 prediction of the heating and cooling loads of a building is an essential aspect in studies involving the analysis of energy consumption in buildings. An accurate estimation of heating and cooling load leads to better management of energy related tasks and progressing towards an energy efficient building. With increasing global energy demands and buildings being major energy consuming entities, there is renewed interest in studying the energy performance of buildings. Alternative technologies like Artificial Intelligence (AI) techniques are being widely used in energy studies involving buildings. This paper presents a review of research in the area of forecasting the heating and cooling load of buildings using AI techniques. The results discussed in this paper demonstrate the use of AI techniques in the estimation of the thermal loads of buildings. An accurate prediction of the heating and cooling loads of buildings is necessary for forecasting the energy expenditure in buildings. It can also help in the design and construction of energy efficient buildings. [ABSTRACT FROM AUTHOR]

The direct load control (DLC) of populations of air conditioners (ACs) is a cost-effective demand-side strategy to manage peak demand and provide ancillary services to the electricity grid. Much research in recent years has focused on the formulation of mathematical models for the aggregate demand of such populations as a basis for DLC analysis and design. These models, however, are sensitive to ambient temperature, which is often assumed constant, and rely on the knowledge of thermal parameters that are typically difficult to obtain. This paper develops a new mathematical model to map the effect of ambient temperature to the aggregate demand response of a heterogeneous population of ACs. The significance of this new model lies in that it can be used to estimate the thermal parameters that characterize aggregate demand using available local weather and demand data, with no need for additional metering or direct engagement of grid users. While the estimation of these parameters is of independent interest in mapping distributed building energy performance over the grid, a key additional benefit is that these parameters fit existing models for DLC design, which provides the technical basis for practical model-based DLC of populations of ACs. [ABSTRACT FROM PUBLISHER]

Featured Application: Optimization of passive solar strategies to minimize building energy demand. Passive solar system design is an essential asset in a zero-energy building perspective to reduce heating, cooling, lighting, and ventilation loads. The integration of passive systems in building leads to a reduction of plant operation with considerable environmental benefits. The design can be related to intrinsic and extrinsic factors that influence the final performance in a synergistic way. The aim of this paper is to provide a comprehensive view of the elements that influence passive solar systems by means of an analysis of the theoretical background and the synergistic design of various solutions available. The paper quantifies the potential impact of influencing factors on the final performance and then investigates a case study of an existing public building, analyzing the effects of the integration of different passive systems through energy simulations. General investigation has highlighted that latitude and orientation impact energy saving on average by 3–13 and 6–11 percentage points, respectively. The case study showed that almost 20% of the building energy demand can be saved by means of passive solar systems. A higher contribution is given by mixing direct and indirect solutions, as half of the heating and around 25% of the cooling energy demand can be cut off. [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.

Cooling and heating energy accounts for a significant portion of the total energy consumption in residential sector. Building envelope is exposed to sunlight and outside air and therefore have a significant role in determining the thermal loads of buildings. Meanwhile, roofs which are exposed to sunlight all the day long are important envelope components and have a significant share of buildings energy consumption. Therefore, applying appropriate roof solutions can significantly reduce building energy consumption for air-conditioning and improve indoor comfort conditions. This paper aims to investigate the effect of different roofing techniques on thermal performance of a single-storey residential building with two types of uninsulated and insulated configurations under different climatic conditions of Iran. For this purpose, different cool roof albedos 0.5, 0.7, and 0.9 and two types of green roofs, GR with actual local rainfall and wet GR, are considered. The thermal loads of the buildings are calculated using the DesignBuilder software and compared with a conventional cast concrete roof. The results show that by choosing an appropriate type of roof technique, the total air-conditioning energy requirement of the building can be reduced between 7-31%, depending on the building configuration and climatic condition. [ABSTRACT FROM AUTHOR]

This paper aims to better understand how building density and shape jointly influence building energy performance in downtown urban environments. Three research questions are addressed: (1) How does the building density influence energy performance? (2) Given the same density, how do different building cover ratios create different impacts on energy performance? And,(3) How do different building typologies affect the density–energy relationship? To explore these questions, a series of parametric simulation experiments were conducted based on a hypothetical urban block structure that mimics the downtown urban grid of Portland. Energy use intensities of the office buildings with fully controlled environments are simulated. In contrast to the hypothesis that energy performance would be enhanced by increasing density, the results suggest that building energy use intensity decreases when density increases to a certain point and then begins to increase. Such a pattern suggests a threshold density that has the minimum building energy use intensity, while other parameters are constant. The study also explores how different building shapes generated with different cover ratio values and building typologies perform in terms of building energy performance, given the same density. Additional experiments extend the findings from Portland to downtown Atlanta and show similar patterns. [ABSTRACT FROM AUTHOR]

Prediction of the energy performance of buildings helps designers with decision-making during the design process in new construction, as well as in renovation projects. Simulation software is used as a prediction tool to calculate the energy performance of buildings. However, numerous studies question its reliability due to the existing discrepancy (gap) between calculated and actual energy performance. Although occupant behaviour is identified as a factor of major impact on the energy performance of buildings, the complex stochastic nature of user behaviour makes it difficult to define actual occupancy patterns. As a result, standard and normative data are usually used as input in energy simulation models. The aim of this research is to test the effect of the use of actual presence profiles on energy demand simulations compared to the use of international normative presence profiles. A study on energy demand has therefore been developed, using dynamic simulation and monitoring campaigns. The results show that the heating and cooling energy demand may differ by up to 15% depending on whether actual or standard presence profiles are used. Therefore, presence profiles should be considered as a significant factor in the adjustment of input data in renovation projects. The final aim of this investigation is to determine the effect of using more accurate building and occupancy simulation parameters when assessing the feasibility of building renovation (payback period calculation for example). This paper focuses on the effect of presence profiles. [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]

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]

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]

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]

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]

Abstract In the last decades, European countries have provided for stringent energy requirements for new buildings. In improving the energy performance of buildings, windows play a significant role as they largely influence the energy need. The windows design should base on the balanced trade-off between the solar heat gains and the heat transfer by transmission. In the paper, for some Italian climatic zones, the relation between the optimal window-to-wall ratio (WWR) and the energy need in residential nearly zero-energy buildings (nZEBs) is investigated. In the case studies, the envelope thermo-physical properties are consistent with the nZEB requirements established at national level. The energy performance assessment is carried out by means of a detailed dynamic simulation tool (EnergyPlus). The influence of different orientations and sizes of windows on the energy performance and the peak power are studied. The paper analyses the effect of WWR in the design of nZEBs. [ABSTRACT FROM AUTHOR]

Tree-based ensemble learning has received significant interest as one of the most reliable and broadly applicable classes of machine learning techniques. However, thus far, it has rarely been used to model and evaluate the drivers of energy consumption in buildings and as such its performance and accuracy in this field have yet to be properly tested or fully understood. The goal of this paper is to evaluate the performance of three ensemble learning algorithms in modelling and predicting the heating and cooling loads of buildings, namely (i) random forests, (ii) extremely randomized trees (extra-trees), and (iii) gradient boosted regression trees. Results show that the tested algorithms outperform the ones proposed in the recent literature, with gradient boosting improving on the prediction accuracy of the second best-performing algorithm by an average of 14% and 65% for the heating and cooling loads, respectively. [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]

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]

Manual solar shades rely on occupants’ control and thus its energy performance is highly related to occupants’ behavior. This paper conducted a co-simulation study on the impact of shade behavior on building energy performance and its uncertainty at different window-to-war ratios (WWRs). A previously developed stochastic behavior model for manual solar shades is used in this paper. Results show that energy uncertainty increases from 1.8% to 3.5% as WWR increases from 25% to 90% and a linear regression was given for predicting energy uncertainty with a determination coefficient of 0.9595. Whether energy uncertainty due to stochastic shade behavior has a significant impact on total energy performance depends on WWR design. To have a high and robust building performance, a small WWR that meets minimum daylighting performance is suggested for office buildings in this climate region. For a more accurate simulation of manual shades, a better consideration of manual shades than existing simplified assumptions by building energy regulations/standards is required.

The gap between the intended and actual energy performance of buildings is increasingly well documented in the non-domestic building sector. Recognition of this issue has led to the availability of a large range of initiatives that seek to ensure energy efficient building operation. This article reviews the practical implementation of three such initiatives in a case study building at the University of Cambridge. The notionally high-performance office/laboratory building implemented two voluntary design frameworks during building planning and construction: the voluntary rating scheme BREEAM and a bespoke Soft Landings framework called the CambridgeWork Plan. The building additionally meets the energy reporting criteria for the EU Energy Performance of Buildings Directive (EPBD), a legislative requirement for many publicly owned buildings in the UK. The relative impact of these three approaches for optimising building energy performance is reviewed through a mixed methods approach of building occupant and operator interviews, document analysis and energy performance review. The building's core functions were revealed to consume 140% more energy than the building logbook estimate for the same needs. This difference, referred to widely as the energy performance gap, is larger than the majority of reported UK university buildings in the energy reporting database CarbonBuzz. The three implemented initiatives are demonstrated to be inadequate for reducing the energy performance gap in the case study, thus a number of alternative energy efficiency approaches are additionally reviewed. Common to the three approaches used in the case study is a lack of verification of actual building performance despite ambitious sustainability targets, due to a heavy focus on the design-stage and few follow-up mechanisms. The paper demonstrates the potential of energy efficiency initiatives that are focussed on operational performance as a core criterion (such as the Living Building Challenge) together with those that ensure the creation of realistic energy estimates at the design stage (such as the Chartered Institution of Building Services Engineers (CIBSE) Technical Memorandum 54). [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]

Uninhabited buildings do not require energy; therefore building energy consumption depends on user habits and domestic appliance equipment. Naturally, building energy performance (BEP) also depends on geometrical, thermophysical and heating, ventilating and air conditioning characteristics, but in this paper, we would like to focus on human behaviour. The European Union energy policy, for example, energy labelling approach or Directive energy performance building directive (EPBD) and EPBD II, adopts labelling strategies to change user choice; the aim of labelling is to improve energy user awareness. Building energy simulation software and Building Standards to evaluate BEP (e.g. European committee for standardization Umbrella) adopt some values from scientific literature, for example, domestic appliance, people presence. The ventilation rate depends on geometry; climate but also they have a main dependence on user and human behaviour. In the present paper, we report on human behaviour studied via a survey questionnaire. The goals of the paper are to estimate whether an online survey questionnaire could be used to understand user behaviour and ventilation rate values. In other words, we would like to know whether an online questionnaire could be used instead of monitoring or questionnaire interviews. Results show that if the questions are simple and basic, the results could be compared with other questionnaire methods. [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]

Purpose: Due to the large quantity of data that are recorded in energy efficient buildings, understanding the behavior of various underlying operations has become a complex and challenging task. This paper proposes a method to support analysis of energy systems and validates it using operational data from a cold water chiller. The method automatically detects various operation patterns in the energy system. Methods: The use of k-means clustering is being proposed to automatically identify the On (operational) cycles of a system operating with a duty cycle. The latter's data is subsequently transformed to symbolic representations by using the symbolic aggregate approximation method. Afterward, the symbols are converted to bag of words representation (BoWR) for hierarchical clustering. A gap statistics method is used to find the best number of clusters in the data. Finally, operation patterns of the energy system are grouped together in each cluster. An adsorption chiller, operating under real life conditions, supplies the reference data for validation. Results: The proposed method has been compared with dynamic time warping (DTW) method using cophenetic coefficients and it has been shown that the BoWR has produced better results as compared to DTW. The results of BoWR are further investigated and for finding the optimal number of clusters, gap statistics have been used. At the end, interesting patterns of each cluster are discussed in detail. Conclusion: The main goal of this research work is to provide analysis algorithms that automatically find the various patterns in the energy system of a building using as little configuration or field knowledge as possible. A bag of word representation method with hierarchical clustering has been proposed to assess the performance of a building energy system. [ABSTRACT FROM AUTHOR]