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Building energy benchmarking is required for adopting an energy certification scheme, promoting energy efficiency and reducing energy consumption. It demonstrates the current level of energy consumption, the value of potential energy improvement and the prospects for additional savings. This paper aims to create a new data envelopment analysis (DEA) model that overcomes the limitations of existing models for building energy benchmarking.

Data preparation: the findings of the literature search and subject matter experts’ inputs are used to construct the DEA model. Particularly, it is ensured that the included variables would not violate the fundamental assumption of DEA modeling, DEA convexity axiom. New DEA formulation: controllable and non-controllable variables, e.g. weather conditions, are differentiated in the new formulation. A new approach is used to identify outliers to avoid skewing the efficiency scores for the rest of the buildings under consideration. Efficiency analysis: three distinct efficiencies are computed and analyzed in benchmarking building energy: overall, pure technical, and scale efficiency.

The proposed DEA approach is successfully applied to a data set provided by a utility management and energy services company that is active in the multifamily housing industry. Building characteristics and energy consumption of 124 multifamily properties in 15 different states in the USA are found in the data set. Buildings in this data set are benchmarked using the new DEA energy benchmarking formulation. Building energy benchmarking is also conducted in a time series manner showing how a particular building performs across the period of 12 months compared with its peers.

The proposed research contributes to the body of knowledge in building energy benchmarking through developing a new outlier detection method to mitigate the impact of super-efficient and super-inefficient buildings on skewing the efficiency scores of the other buildings; avoiding ratio variables in the DEA formulation to adhere to the convexity assumption that existing DEA methods do not follow; and distinguishing between controllable and non-controllable variables in the DEA formulation. This research contributes to the state of practice through providing a new energy benchmarking tool for facility managers and building owners that strive to relatively rank the energy-efficiency of their properties and identify low-performing properties as investment targets to enhance energy efficiency.

The purpose of this study is to present the theoretical framework of the “data envelopment analysis (DEA) Energy Management System (DEMS)” proposed to assist individual departments occupying the same buildings on university campus in assessing the energy efficiencies of their facilities, as well as to demonstrate the implementation results of the DEMS applied in the case of the Department of Architecture of NTUST in Taiwan.

The proposed DEMS considers each “space” within a department in a given “time” (such as a month) as a decision-making unit (DMU). Then, regression analysis is performed on data of “existing environment”, “occupancy” factors and “actual energy consumption EUI (energy usage intensity)” related variables. The regression equation derived is then used to calculate the “predicted EUI” for all DMUs. The “actual EUI” is further considered as the input data and the “predicted EUI” as the output data of the DEMS, on which data envelopment analysis is conducted to produce three types of energy-efficiency scores (overall efficiency, scale efficiency, pure technical efficiency) to indicate the energy efficiencies of all DMUs.

The DEMS was developed and further implemented in the Department of Architecture of NTUST in Taiwan to illustrate how it can be used to assist individual departments within universities in assessing the energy management effectiveness of their spaces.

The accuracy of the energy-efficiency scores depends greatly on the accuracy of the predicted EUIs of spaces, and, therefore, it is critical to identify a better regression model with higher predictability (R2). The relatively low actual EUIs of certain student spaces during winter and summer breaks may greatly affect the resulting energy-efficiency scores.

The DEMS allows facility managers to assess and compare the energy-efficiency scores “among different spaces”, to further review the energy efficiency of a space “over time” and to recognize the benchmark cases and pursue actions for energy improvement.

This study explores the research concepts of “space type” and “internal benchmark” with an analytical method “data envelopment analysis” to assess the energy efficiency of an individual department which may only occupy certain floors of a building.

The purpose of this paper is to identify energy efficiency metrics that can be used by IT managers to measure and maintain the implementation of cost savings and green initiatives in data centers.

The paper looks at the background of the problem and explores the reasons why energy savings in the data center are an important issue. Included are interviews and survey results from IT professionals serving at four unique organizations. A model of the measurable components of a data center is created to provide a framework for organizing metrics and communicating results throughout the corporation. The strengths and weaknesses of two of the most common data center metrics, PUE and DCP, are examined closely.

The paper concludes with future metric recommendations and a proposed credit‐based system that could be applied to encourage closer management of these metrics.

The metric recommendations can be used by IT managers resulting in energy efficiency improvements in their data centers.

The paper provides a good comprehension of multiple approaches and makes recommendations for a platform metric that can be further developed and adopted as a standard.

This paper aims to primarily analyzing the state and pattern of current energy benchmarking progress on commercial buildings since the New York City’s energy disclosure law, Local Law 84: Benchmarking has been implemented. It then compares the yearly benchmarking progress of Leadership in Energy and Environmental Design (LEED)-certified and non-LEED-certified buildings as well as ENERGY STAR-certified and non-ENERGY STAR-certified.

For thorough analytics, the authors combined and examined four sources of data: New York City Local Law 84: Benchmarking, Primary Land Use Tax Lot Output, US Green Building Council and US Environmental Protection Agency. The data sets were combined using two primary keys: the Borough, Block, Lot (BBL) number and the building address. Four years of energy use intensity values were obtained and normalized by shrinking the range of deviance in weather.

The findings indicate a significant improvement in the benchmarking progress when controlling building size, building type, year of construction or the most recent renovation and the presence of renovation. Interestingly, there is no significant difference in the energy benchmarking progress between LEED- and non-LEED-certified buildings. Possible reasons are explored and discussed.

From a methodological perspective, the study benefited from data disclosure as well as open data sources and used secondary data with a relatively large sample size. Many studies in the construction industry are based on the case-study approach, which may affect generalizability and causality of research findings. This unique approach illustrates the potential of secondary data analysis in the industry.

The dearth of green standards (GS) in sub-Saharan Africa is alarming and the green cost premiums (GCP) in seeking certification in emerging markets are scanty. This paper studied the Building Energy-Efficiency Code of Nigeria (BEEC) and estimated the potential GCPs associated with the various energy-efficiency ratings.

The study retrofitted 150 conventional residential bungalow and maisonette buildings using BEEC's energy-efficiency interventions and performed analytical estimating of the retrofitted designs. The mean cost premium associated with each energy-efficiency intervention is presented as well as their financial benefits and payback periods. The benefits are achievable financial-savings due to a reduction in energy consumption and savings in electricity payment estimated from the average energy demands of each building. An independent t-test was further conducted to determine the cost differential between energy-efficient design (ED) and conventional design over a five-year period.

The potential GCPs and their payback periods are actually less than feared. The study showed that less than 5% and 21% extra funding would be required to achieve 1 to 4-Star and 5-Star energy-efficiency ratings involving passive design interventions and photovoltaic systems. Passive and active design interventions produced a financial savings of $8.08/m² in electricity payment and $2.84/m² per annum in energy consumption reduction. The financial-savings ($10.92/m²) was objective to pay-off the GCPs in less than four years. The independent t-test analysis showed the cost of ED is more economical after four years into the project lifecycle.

The research provides cost benchmarks for navigating cost planning and budgetary decisions during ED implementation and births a departure point for advancing energy-efficient construction in developing markets from the rational economic decision perspective.

The purpose of the paper is to examine the monitoring of electrical energy consumption, measures adopted for reducing energy consumption, barriers to energy efficiency improvement and driving forces for energy efficiency improvement in three industrial clusters. It is intends to capture the managerial perspectives on energy saving practices and to identify the possible energy saving opportunities in small and medium enterprises (SMEs).

Three industrial clusters were identified for the study. Research instrument based in-person survey was conducted in which the authors directly administered the questionnaire to all the 181 organisations. This was thought of to facilitate not so well-educated respondents. The survey took about six months in which 110 units responded. Descriptive statistics, exploratory factor analysis and path analysis were used to draw inferences.

There is ample scope for energy savings in the studied clusters. Energy efficiency in many organisations has deteriorated. Their attitude to embrace new or modern technology is shunning. Management’s belief that prevailing technology is efficient, lack of skilled labour, lack of accessibility to updated or modern technology, and lack of compatibility of new technology are found to be the barriers to energy efficiency improvement. Benchmarking by appropriate governments and publicly financed energy auditing act as the driving forces.

The SMEs must use simple yet powerful energy auditing practices on regular basis to reduce energy consumption. This will not only result in lesser energy costs but also lessen the burden on environment. As these are predominantly small enterprises, appropriate governments interventions are essential to bring the desired change.

The operation of chiller systems could account for considerable electricity consumption in air‐conditioned buildings in subtropical regions. The purpose of this paper is to consider using data envelopment analysis (DEA) to facilitate management of their energy performance.

A system serving an institutional building was studied, which contains five sets of chillers, pumps and cooling towers. The building has a total floor area of about 25,000m² and comprises classrooms, lecture theatres, offices and laboratories. The scale, technical and overall efficiencies defined in DEA were calculated based on the correlation between the output variable – system coefficient of performance (COP) – and the input variables – load factor and temperatures of chilled water and condenser water. The efficiencies were further examined to explain how outside air temperatures and controllable variables affect the system performance.

The paper reveals that existing energy management gives a technical efficiency of 0.85 and fine‐tuning the temperature‐related variables could achieve an electricity saving of 14.8 per cent.

The improved COP predicted by DEA is related only to fine‐tuning of the input variables concerned. An increase of COP by other advanced controls or system upgrades should be assessed based on robust system modelling techniques. Yet the extent of COP improvements helps investigate energy management opportunities requiring no or insignificant capital investment on existing systems.

A systematic approach to performing energy management of a chiller system is proposed. The DEA helps examine which operating variable should be fine‐tuned to achieve the highest possible performance.

It is an under researched area to consider using scale and technical efficiencies in DEA to explain energy management of chiller systems and to estimate the highest achievable performance.

The purpose of this paper is to analyze a successful sustainability program run by an owner that has invested $23 million, received rebates of $10 million, accrued over $9 million of savings and has won top scores in LEED and Energy Star. Other owners planning to invest in energy conservation and sustainability can apply the lessons learnt to overcome common barriers.

This case study is based on project information supplied by the owner and structured interviews with the operational team. The projects are analyzed based on drivers and payback characteristics. Finally, the case study puts Adobe Systems' results within the context of the industry by matching it to the challenges identified in other reports.

The results show that 40 percent of projects are initiated by operation management personnel. The projects with the biggest savings are supported by third-party incentives. Only 10 percent of projects are evaluated by simulation and account for 12 percent of annual savings. Energy Star plays a crucial role for benchmarking performance and should be run annually. LEED EB is valuable when expending conservation efforts beyond energy aspects to sustainability. Performance benchmarking is a crucial step to determine the potential and priority of energy improvements.

The findings are based on the three towers in San Jose, California.

Building owners can incorporate the methodologies applied to evaluate these successful projects into their buildings. Facility managers can leverage the findings to present the advantages of recertification and commissioning.

A detailed project analysis, from a leader in practice, shows the importance of the local building operations team in sustainability and energy conservation.

This paper investigates the energy performance of aquatic centres in Victoria.

Physical and occupancy characteristics and energy consumption from various centres were analysed to understand the interrelationship between numerous factors that contribute to the energy consumption of these facilities.

The energy usage intensity of the facilities ranged from 632 to 2,247 kWh/m2 or 8 to 17 kWh/visit. Primary and secondary indicators were examined to find the key performance indicators.

This study sheds some light into the overall energy performance of aquatic centres in the temperate climate of Australia. More samples need to be collected to perform rigorous statistical analysis leading to a reliable benchmark model. System-wise investigation of energy consumption is required to determine where the energy is being used and the saving potentials of each system.

This study has arisen from the need of managers of large aquatic and recreation facilities to benchmark the energy consumption of their own facilities. This study will fill the gap that currently exists in the area of energy rating systems for aquatic centres.

The results of this study showed that aquatic centres consume around seven times more energy than a commercial office building. Thus, if the energy consumption of aquatic centres could be reduced by as little as only 10 per cent, at least 3.5 million tonnes of carbon dioxide emission can be reduced.

Environmental design standards for aquatic centres have generally been overlooked due to the complex nature of these buildings. As a result, this sector suffers from a general lack of both qualitative and quantitative information and benchmarking.

This paper aims to examine the performance efficiency of 56 real estate assets within the rental sector in the UAE to evaluate the relative operation efficiency in relation to revenue generation.

The data envelopment analysis (DEA) approach was used to measure the relative operational efficiency of the studied assets in relation to the revenue performance. This method could produce a more informed and balanced approach to performance measurement.

The outcomes show that scores of efficiencies ranging from 7% to 99% in some of the models. The results showed that on average buildings are 75% relatively less efficient in maintenance, in term of revenue generation, than the benchmark set. Likewise, on average, the inefficient buildings are 60% relatively less efficient in insurance. Result also shows that 95% of the building assets in the sample are by and large operating at decreasing returns to scale. This implies that managers need to considerably reduce the operational resources (input) to improve the levels of revenue.

This study recommends that the FM operational variables that were found to inefficiently contribute to the revenue should be re-examined to test the validity of the findings. This is necessary before generalising or interpolating the results that are presented in this study.

The information obtained about operational performance can help FM managers to understand which improvements in the productivity of inefficient FM resources are required, providing insight into how to reduce operating costs and increase revenue.

This paper adds value in using new FM operational parameters to evaluate the efficiency of the performance of built assets.