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Highlights • The accuracy of current CIBSE TM benchmark for University Campus (UC) was assessed. • A new revised benchmark called UCrb was presented which is more accurate than the current benchmark. • There is a difference of 110 kWh/m2/yr between UC and UCrb which is a significant improvement. • A new methodology for thermal energy estimation at the monthly scale was developed. Abstract According to Chartered Institution of Building Services Engineers, CIBSE TM46 benchmarks, a typical college building needs 240 kWh/m2 thermal energy annually. This paper firstly analysed the accuracy of the CIBSE TM46 ‘University Campus’ benchmark against the thermal consumption values displaying on Display Energy Certificates (DECs). The DECs of 52 typical college buildings in Dublin were assessed and based on the 46% discrepancy between the median of samples and the CIBSE benchmark a new benchmark of 130 kWh/m2/yr was recommended. Secondly, monthly thermal energy models (MTEMs) which take into account the variations of heat demand during a year were generated. MTEMs were created using five key factors, including the mixed use method, CIBSE UC recommended benchmark, activities area, heating degree days, and typical operational hours of heating systems. The high level of accuracy of MTEMs was assessed against the actual consumption. The developed methodology predicts an accurate and detailed estimation of monthly thermal energy consumption in typical college buildings. In addition, a new definition for typical college buildings which is a mixed use building including seven major activities is presented. [ABSTRACT FROM AUTHOR]

Highlights • A multi-level building energy consumption benchmarking index system for cooling in large commercial buildings is presented. • The recommended index value is calculated based on real operation data of a commercial building in China. • Evaluation efficiency is improved by using a simplified calculation method. • The benchmarking method was validated using real data from eight large shopping centers in China. Abstract Urban development rapidly increases the total area and the energy consumption of large commercial buildings. Accordingly, the total energy consumption is the focus of numerous energy saving studies. An optimal benchmarking system provides a recommended energy consumption level for each building as well as identifying and prioritizing challenges to energy saving in each building. Based on detailed sub-metering system data and building operational data, this paper presents a simplified energy consumption benchmarking method for air-conditioning systems that cool large commercial buildings. Firstly, a multi-level benchmarking index system is established. Next, the energy performance data of eight large shopping centers in China validates the simplified benchmarking method and finally, the energy performance data of the participating shopping centers is analyzed under the present method. Our data focus on the energy saving potential, as well as informing improvement strategies for building energy performance, that can be used for efficient energy benchmarking process for cooling in large-scale commercial buildings. [ABSTRACT FROM AUTHOR]

[Display omitted] The school building sector has a pivotal role to play in the transition to a low carbon UK economy. School buildings are responsible for 15% of the country's public sector carbon emissions, with space heating currently making up the largest proportion of energy use and associated costs in schools. Children spend a large part of their waking life in school buildings. There is substantial evidence that poor indoor air quality and thermal discomfort can have detrimental impacts on the performance, wellbeing and health of schoolchildren and school staff. Maintaining high indoor environmental quality whilst reducing energy demand and carbon emissions in schools is challenging due to the unique operational characteristics of school environments, e.g. high and intermittent occupancy densities or changes in occupancy patterns throughout the year. Furthermore, existing data show that 81% of the school building stock in England was constructed before 1976. Challenges facing the ageing school building stock may be exacerbated in the context of ongoing and future climate change. In recent decades, building stock modelling has been widely used to quantify and evaluate the current and future energy and indoor environmental quality performance of large numbers of buildings at the neighbourhood, city, regional or national level. Building stock models commonly use building archetypes, which aim to represent the diversity of building stocks through frequently occurring building typologies. The aim of this paper is to introduce the Data dRiven Engine for Archetype Models of Schools (DREAMS), a novel, data-driven, archetype-based school building stock modelling framework. DREAMS enables the detailed representation of the school building stock in England through the statistical analysis of two large scale and highly detailed databases provided by the UK Government: (i) the Property Data Survey Programme (PDSP) from the Department for Education (DfE), and (ii) Display Energy Certificates (DEC). In this paper, the development of 168 building archetypes representing 9,551 primary schools in England is presented. The energy consumption of the English primary school building stock was modelled for a typical year under the current climate using the widely tested and applied building performance software EnergyPlus. For the purposes of modelling validation, the DREAMS space heating demand predictions were compared against average measured energy consumption of the schools that were represented by each archetype. It was demonstrated that the simulated fossil-thermal energy consumption of a typical primary school in England was only 7% higher than measured energy consumption (139 kWh/m2/y simulated, compared to 130 kWh/m2/y measured). The building stock model performs better at predicting the energy performance of naturally ventilated buildings, which constitute 97% of the stock, than that of mechanically ventilated ones. The framework has also shown capabilities in predicting energy consumption on a more localised scale. The London primary school building stock was examined as a case study. School building stock modelling frameworks such as DREAMS can be powerful tools that aid decision-makers to quantify and evaluate the impact of a wide range of building stock-level policies, energy efficiency interventions and climate change scenarios on school energy and indoor environmental performance. [ABSTRACT FROM AUTHOR]

Energy signatures for air conditioning systems can have characteristics which are not seen with heating systems. This paper explains and illustrates some of the characteristics that are specific to air conditioning systems and describes how energy signatures that take account of them can be applied to produce benchmarks, control charts and diagnostic information. It focusses on the use of energy signatures derived from measured daily system energy consumption. Daily energy signatures can generate more robust energy consumption benchmarks and provide additional insight into unusual energy demand patterns compared to monthly or weekly signatures, albeit requiring slightly more data. In particular, they distinguish between weekday and weekend consumptions. They can be used to generate benchmarks based on standardised annual consumption or standardised annual load factor. In addition they can be used to generate control charts to identify days of unusual consumption for individual systems. More sets of daily energy consumption data are needed to evaluate their diagnostic power. [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]

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: The knowledge of building stock energy data of a country is a very significant tool for energy benchmarks establishment, energy rating procedures and building classification boundaries determination, according to the Directive 2002/91/EC and its implementation in EU Member States. The lack of building energy databases in many EU Countries, including Greece, and the difficulties of collecting them lead to the investigation of other potential solutions. The aim of this paper is to present a method of a Virtual Building Dataset (VBD) creation for office buildings in Greece. The philosophy of VBD is based on the creation and simulation of random office buildings that could be found or built in Greece, taking into account the Greek constructional and operational characteristics of office buildings and Greek legislation. The VBD consists of 30,000 buildings (10,000 in each climatic zone) with their detailed constructional and operational data and of their simulation outputs: the annual specific energy consumption for heating, cooling, artificial lighting, office equipment and an indoor thermal comfort indicator. Based on VBD results the energy and indoor thermal comfort benchmarks for office building sector in Greece are assessed and presented. [Copyright &y& Elsevier]

Abstract: Energy certification schemes for buildings emerged in the early 1990s as an essential method for improving energy efficiency, minimising energy consumption and enabling greater transparency with regards to the use of energy in buildings. However, from the beginning their definition and implementation process were diffuse and, occasionally, have confused building sector stakeholders. A multiplicity of terms and concepts such as energy performance, energy efficiency, energy ratings, benchmarking, labelling, etc., have emerged with sometimes overlapping meanings. This has frequently led to misleading interpretations by regulatory bodies, energy agencies and final consumers. This paper analyses the origin and the historic development of energy certification schemes in buildings along with the definition and scope of a building energy certificate and critical aspects of its implementation. Concepts such as benchmarking tools, energy ratings and energy labelling are clarified within the wider topic of certification schemes. Finally, a seven steps process is proposed as a guide for implementing building energy certification. [Copyright &y& Elsevier]

• System-level key performance indicators are missing in the literature yet important. • A suite of system key performance indicators (KPIs) are defined. • The system KPIs cover four major end use systems and their eleven sub-systems. • A database of system KPIs is developed using simulation results of 16 prototype building models. • Potential applications of system KPIs include system performance benchmarking and diagnostics, code compliance, and M&V. Quantifying building energy performance through the development and use of key performance indicators (KPIs) is an essential step in achieving energy saving goals in both new and existing buildings. Current methods used to evaluate improvements, however, are not well represented at the system-level (e.g., lighting, plug-loads, HVAC, service water heating). Instead, they are typically only either measured at the whole building level (e.g., energy use intensity) or at the equipment level (e.g., chiller efficiency coefficient of performance (COP)) with limited insights for benchmarking and diagnosing deviations in performance of aggregated equipment that delivers a specific service to a building (e.g., space heating, lighting). The increasing installation of sensors and meters in buildings makes the evaluation of building performance at the system level more feasible through improved data collection. Leveraging this opportunity, this study introduces a set of system-level KPIs, which cover four major end-use systems in buildings: lighting, MELs (Miscellaneous Electric Loads, aka plug loads), HVAC (heating, ventilation, and air-conditioning), and SWH (service water heating), and their eleven subsystems. The system KPIs are formulated in a new context to represent various types of performance, including energy use, peak demand, load shape, occupant thermal comfort and visual comfort, ventilation, and water use. This paper also presents a database of system KPIs using the EnergyPlus simulation results of 16 USDOE prototype commercial building models across four vintages and five climate zones. These system KPIs, although originally developed for office buildings, can be applied to other building types with some adjustment or extension. Potential applications of system KPIs for system performance benchmarking and diagnostics, code compliance, and measurement and verification are discussed. [ABSTRACT FROM AUTHOR]

• 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]