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Highlights • Review the existing data sources of US commercial building energy usage. • Compare energy results from survey and simulation data for US commercial buildings. • Discuss the different applications of existing data sources for US commercial building energy. • Provide the cases of applications to guide the users to use the data sources. Abstract U.S. commercial buildings consumed around 18% of total primary energy in 2017 and a 2.23 EJ increase is expected by 2050. Energy-related data for commercial buildings can be used for various applications, including benchmarking, building component analysis, market potential analysis, and policy making. Although there are plenty of data sources for energy usage in commercial buildings, they have not been thoroughly reviewed and summarized. As a result, users do not have comprehensive guidelines about selections of right data sources for specific application needs. To fill this gap, this paper conducts a comprehensive review to summarize data sources for energy usage in U.S. commercial buildings and discuss their usages for different applications. First, the paper summarizes the survey and simulation data sources for energy usage. The data sources are compared in terms of their data collection methods, released information, and relevant features. Second, this paper analyzes the applications for different survey and simulation data sources. This review categorizes the applications of data sources into five categories, including energy performance benchmarks, energy use forecasts and predictions, energy use contributions of building components, supports of building energy policies, and urban-scale energy use analysis. Moreover, the paper introduces several cases to demonstrate the usages of these data sources. [ABSTRACT FROM AUTHOR]

[Display omitted] • 33 worldwide open datasets of city-level building energy consumption are summarized. • Representative applications of 198 papers using the collected datasets are analyzed and compared. • Policy implications are presented for using the open datasets to support policymaking. • Useful non-energy datasets about weather, building features, urban forms, and occupant-related factors are recommended. • Solutions to privacy issues are explored, including anonymizing methods and proper data aggregation. Data related to building energy use fuels the research and applications on building energy efficiency, which is an essential measure to address global energy and environmental challenges. However, in most cities, there is a lack of comprehensive and publicly accessible building energy use datasets with necessary temporal and spatial granularities to support urban building energy modeling, regional energy planning, city-level building performance benchmarking, and policymaking on building energy efficiency. Data owners and governments are facing challenges in determining which and how building energy use data should be disclosed at the city level and how to protect data privacy. This review paper provides insights to answer these questions based on a comprehensive and critical review of worldwide open datasets and their applications in the built environment context. Detailed information about the collected 33 building energy datasets is summarized and categorized. Studies identified into 11 subdomains using these open datasets are critically analyzed and compared. Potential policy implications based on the studies are also proposed. Moreover, non-energy datasets that are frequently used in research relevant to urban building energy use are also introduced. Solutions to privacy issues are discussed to address concerns from data publishers. Finally, significant conclusions are made to support the proper disclosure of city-level building energy data. This review study is valuable to urban building energy data disclosure, urban building energy modeling, and data-driven energy policymaking. [ABSTRACT FROM AUTHOR]

Highlights • The first city-level building energy efficiency benchmarking system for China. • A model considering the influence of operation parameters on energy consumption. • A systematic methodology of building energy benchmarking is proposed. • The model is a valuable analysis support tool for building energy managers. Abstract The city-level public building energy efficiency benchmarking system has an important guiding value for building energy-saving work, especially for Beijing, with its high energy-consumption density and both long heating and air-conditioning periods. This paper analyzed the characteristics of large-scale office buildings in Beijing and gives a detailed description of the process of establishing a public building energy efficiency benchmarking system for large-scale office buildings in Beijing, China, including data investigation and verification, Energy Usage Intensity(EUI) calculation, determining independent and dependent variables, developing and testing the benchmarking model, and deriving the benchmarking rating table. Then the first city-level public office building energy efficiency benchmarking system in China is established based on 88 large-scale office buildings in Beijing. The results showed that the model's performance index R2 is 0.667 and gives a good representation. The process has a strong potential for replication for other cities in China and even for a national energy-consumption evaluation system. [ABSTRACT FROM AUTHOR]

Abstract: The EU Member States are in the process of implementing energy rating procedures for buildings. For non-domestic buildings in particular, devising a robust and cost effective energy rating method is not a simple task. The situation becomes more complicated where countries do not have a tradition of performing energy calculations or undertaking energy measurements in buildings. This paper outlines a methodology to develop energy benchmarks and rating systems starting from the very first step of data collection from the building stock. Methods for rating a sample Irish school according to both calculated and measured ratings are applied, and finally the paper discusses the advantages and disadvantages of the two approaches. [Copyright &y& Elsevier]

With the world rapidly urbanizing, addressing the energy intensive urban built environment is becoming increasingly important. Cities across the United States and the world are turning to energy benchmarking as a means of understanding the relative energy efficiency of their building stock and identifying potential opportunities to reduce energy usage. Benchmarking utilizes building characteristics and energy use data to measure a building’s energy consumption against a performance baseline and derive a level of energy efficiency. Over twenty cities in the United States and many others across the world have passed laws mandating the collection and disclosure of energy use data to enable benchmarking and pinpoint potential energy saving opportunities. However, municipalities are struggling to convert this data into actionable insights and identify which buildings are prime candidates for energy efficiency interventions. Although an extensive body of work exists on benchmarking building energy performance, previous works are limited in their ability to leverage such emerging data streams and conduct analysis at the city scale. Moreover, previous methods are largely based on black-box models that limit the interpretability of results and in turn hinder the ability of policy-makers to employ such models in their policy design and decision-making processes. In this paper, we propose DUE-B, a new Data-driven Urban Energy Benchmarking methodology based on recursive partitioning and stochastic frontier analysis. To test DUE-B, we evaluate its performance using real energy and building data from over 10,000 buildings in New York City, and we compare the results to other common benchmarking models using the Kendall tau-b correlation coefficient. Results indicate that DUE-B is more robust than conventional benchmarking methods in respect to identifying subsets of efficient and inefficient buildings. Furthermore, we highlight how results from DUE-B can be utilized by municipal officials and other policy-makers to target inefficient buildings for energy efficiency interventions, incentives, and programs. Specifically, we indicate how DUE-B can be utilized by municipalities to target the most inefficient buildings for subsidized onsite energy audits and less inefficient buildings for less capital-intensive energy efficiency strategies such as incentives and educational programs. In the end, more robust benchmarking methods like DUE-B have the potential to enhance the efficacy of municipal energy efficiency programs and help transition cities to a more sustainable energy future. [ABSTRACT FROM AUTHOR]

Evaluation methods can be used to determine what constitutes good energy performance in a building. With an increasing focus on energy use of buildings worldwide, this evaluation assumes a fundamental importance. This paper provides a comprehensive review of the available methods for analysing, classifying, benchmarking, rating and evaluating energy performance in non-domestic buildings. Methodologies are grouped in five categories: engineering calculations, simulation, statistical methods, machine learning and other methods. Techniques for evaluating buildings are described, their principal applications are shown and limitations are identified. The use of performance evaluation in energy efficiency programmes and standards is mapped. There is a need to further develop interactions between the main modelling techniques to produce simple, robust and validated models. Also, evaluation techniques must be developed to consider comfort or service provision in the buildings as a factor in energy performance. [ABSTRACT FROM AUTHOR]

Sport centers and more specifically swimming pools are regarded as high energy consuming installations and also major air pollutants, contributing to Green House Gas (GHG) emissions as their demand in energy is extremely large. Taking into consideration EU mandating directives and policies in accordance with this high energy consumption, it is obvious that establishing guidelines for swimming pools in order to improve their energy performance by implementing energy conservation actions and increasing their energy efficiency will consequently result to significant energy savings. The aim of this paper is to assess energy performance of outdoor swimming pools operating in Greece. The approach followed is a bottom-up approach as it first analyses the characteristics of all operating swimming pools based on data retrieved from General Secretary of Sports, questionnaires and specific energy audits and surveys, with the aim of quantifying their energy performance. Examining the performance of swimming pools, which mainly use conventional oil or gas fired boilers to cover their thermal needs, it is made clear that there is a broad field to apply energy conservation measures and achieve financial and environmental benefits. [ABSTRACT FROM AUTHOR]

Low energy buildings and zero energy buildings have attracted increasing attention in both academic and professional fields. The performances of these buildings are largely affected by the design of the renewable energy systems. This paper presents a comparison study on two design optimization methods for renewable energy systems in these buildings, including a single objective optimization using Genetic Algorithm and a multi-objectives optimization using Non-dominated Sorting Genetic Algorithm (NSGA-II). Building energy system models and renewable energy system models are developed and adopted, allowing the consideration of the interaction between building energy systems and renewable energy systems in optimization. Two case studies are conducted to evaluate the capability and effectiveness of proposed optimization methods, based on the Hong Kong Zero Carbon Building. The performances of the buildings with the renewable energy systems optimized by both methods are much better than that of the benchmark building in most scenarios. The single objective optimization can provide the “best” solution directly for a given objective while the multi-objective optimization provides rich information for designers to make better compromised decisions. [ABSTRACT FROM AUTHOR]

Though many building energy benchmarking programs have been developed during the past decades, they hold certain limitations. The major concern is that they may cause misleading benchmarking due to not fully considering the impacts of the multiple features of buildings on energy performance. The existing methods classify buildings according to only one of many features of buildings—the use type, which may result in a comparison between two buildings that are tremendously different in other features and not properly comparable as a result. This paper aims to tackle this challenge by proposing a new methodology based on the clustering concept. The clustering concept, which reflects on machine learning algorithms, classifies buildings based on a multi-dimensional domain of building features, rather than the single dimension of use type. Buildings with the greatest similarity of features that influence energy performance are classified into the same cluster, and benchmarked according to the centroid reference of the cluster. The proposed methodology contains four steps: feature selection, clustering algorithm adaptation, results validation, and interpretation. The experimentation was carried out with a comparison between the proposed methodology and the Energy Star approach. It was shown that the proposed methodology could account for the total building energy performance and was able to provide a more comprehensive approach to benchmarking. In addition, the multi-dimensional clustering concept enables energy benchmarking among different types of buildings, and inspires a new perspective to investigate building performance typology. [ABSTRACT FROM AUTHOR]

Building energy use benchmarking is the process of measuring the energy performance of buildings relative to their peer group for creating awareness and identifying energy-saving opportunities. In this paper, we present the design and implementation of BEEM , a data-driven energy use benchmarking system for buildings in Singapore. The peer groups for comparison are established using a public energy disclosure data set. We use an ensemble tree algorithm for accurately modeling building energy use and for identifying the most influential factors. Our models reduce the prediction error from 24.39% to 6.04%, on average, when compared to the baseline linear regression models, which were used in the previous energy efficiency labeling program in Singapore, and outperforms ten other recent models. Using the prototype implementation of BEEM, we benchmarked three building types, office (290), hotel (203), and retail (125), and compared their rating. The code repository and the accompanying data set are released as an open-source project for community use. [ABSTRACT FROM AUTHOR]

Abstract: BREEAM, LEED, CASBEE, BEAM Plus and Chinese ESGB have been formally launched at different stages in the last twenty years. How well the certified and rated buildings compare with each other is always an interest of building designers and policy-makers. This paper presents a side-by-side comparison of energy use assessments of the five schemes. The comparisons are on the assessment method and criteria, default parameters, trade-offs allowed, performance scales, approved simulation tools, performance indicators and assessment results. Comparison results showed that all the five schemes are based on relative performance. It was also found that despite the variations in default parameters among the five schemes, market positions of certified buildings are comparable. LEED was found to be the most stringent and relatively less flexible in its assessment criteria. Nonetheless, the energy cost budget approach, adopted only by LEED, is able to fulfil emission reduction and cost saving objectives on the condition that a “fuel neutral” approach should be adopted. [Copyright &y& Elsevier]

Abstract: This paper provides a summary of an energy benchmarking study that uses performance data of a sample of Information Technology facilities in Bangalore. Information provided by the sample of occupiers was used to develop an Energy Performance Index (EPI) and an Annual Average hourly Energy Performance Index (AAhEPI), which takes into account the variations in operation hours and days for these facilities. The EPI and AAhEPI were modelled to identify the factors that influence energy efficiency. Employment density, size of facility, operating hours per week, type of chiller and age of facility were found to be significant factors in regression models with EPI and AAhEPI as dependent variables. Employment density, size of facility and operating hours per week were standardised and used in a separate regression analysis. Parameter estimates from this regression were used to normalize the EPI and AAhEPI for variance in the independent variables. Three benchmark ranges – the bottom third, middle third and top third – were developed for the two normalised indices. The normalised EPI and AAhEPI of LEED rated building, which were also part of the sample, indicate that, on average, LEED rated buildings outperform the other buildings. [ABSTRACT FROM AUTHOR]

Abstract: While most studies focus on energy savings during occupied hours, this paper shows the shocking quantities of energy wasted during non-occupied hours in commercial buildings. At least five detailed energy audits were carried out in the hot and dry climates of Botswana and South Africa. The work shows that more energy is used during non-working hours (56%) than during working hours (44%). This arises largely from occupants’ behaviour of leaving lights and equipment on at the end of the day, and partly due to poor zoning and controls. There is a crying need for building occupants to learn to switch off what they do not use. The golden rule is: “If you don’t need it, don’t use it!” This is the simplest and cheapest lesson with amongst the biggest savings. Apart from the above, the work also contributes to several other fields of scientific research: it helps in development of benchmarks from sub-hourly field data; it contributes apportionment of energy amongst sub-systems of HVAC, lighting and office equipment; it provides a picture from cooling dominated climates, which normally differs from the largely researched heating dominated climates; it contributes to development of diversity profiles necessary for improvement of simulation accuracy. [Copyright &y& Elsevier]

Abstract: A benchmark study of the energy efficiency of private office buildings in Hong Kong was conducted in 2002 because energy efficiency was declining. In the study, private office buildings were divided into five user groups. For each group, a multiple regression model was developed to find the relationship between Energy Use Intensities (EUIs) and other factors, such as operating hours, for normalization and benchmarking purposes. In this paper we make use of the regression results to study the energy efficiency of private office buildings by different grades. In Hong Kong, office buildings are divided into three grades (A, B, and C) based on the quality of the facility, which is reflected in rental values; a Grade A office building denotes expensive luxury. We found that the EUI of Grade A office buildings is the highest, consuming over 50% of the total energy used in office buildings. Recently, the annual EUI of office buildings has improved even though Grade A floor space is increasing. This may be due to the promotion of the energy efficiency program launched in the last decade. [Copyright &y& Elsevier]

• We selected three benchmarking methods to establish building energy benchmarking models based on a sample of 45 hotel buildings in China. • We compared the three benchmarking methods in terms of consistency, robustness and explanatory ability. • The energy efficiency performance obtained from the three methods were found to be inconsistent. • We came up with suggestions based on comparative analysis for policy-makers to develop building energy benchmarking programs. A reasonable building energy efficiency benchmarking program plays an important role in energy consumption control and supervision. Previous studies have focused on the process of establishing a single benchmarking method, but few have compared the performances and outcomes of different methods. To fill this gap, this paper selects three benchmarking methods—multiple linear regression (MLR) based on Energy Star, stochastic frontier analysis (SFA) and the descriptive statistics method (DSM) based on the national energy consumption standard in China—to develop benchmarking models. We demonstrate each method using data on the energy and building characteristics of 45 four- and five-star hotel buildings located in Chongqing, China. To compare the consistency, robustness and explanatory ability of the three methods, we first utilize the Spearman rank correlation analysis to test whether these methods have consistent energy efficiency ranks and then present Sankey diagrams to further reveal the interactions of the estimated energy efficiency grades obtained from the three methods. It is found that the results of DSM and SFA are most consistent, while MLR vs. SFA and MLR vs. DSM present significant differences in evaluating building energy performance. In addition, DSM is more robust for evaluating the ranks of sampled buildings, while SFA is more robust for evaluating energy efficiency grades. Furthermore, we discuss the explanatory ability of each method. In addition to the building characteristics, the design and operational characteristics of the HVAC system have great effects on building energy consumption. Finally, we present suggestions for policy-makers regarding the development and implementation of the building energy benchmarking program in Chongqing and for the management of buildings with different energy performances to further improve the energy efficiency. [ABSTRACT FROM AUTHOR]