A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A Data Envelopment Analysis Model for Building Energy Efficiency Benchmarking
Building energy benchmarking is required for adopting an energy certification scheme, promoting energy efficiency, and reducing energy consumption. Several methods, such as data envelopment analysis (DEA), were proposed to perform building energy benchmarking. However, existing DEA building energy benchmarking models are subject to several limitations. Current DEA models are sensitive to data outliers such that even a single outlier can result in dramatic changes in efficiency scores of all buildings in a peer group of buildings. In addition, current DEA models cannot determine whether change of the energy-efficiency score of a building is a result of the change of energy efficiency in the building itself or the change of energy efficiency in the peer group of buildings. The objective of this research is to create a peer-wise building energy benchmarking model based on a novel DEA method that is capable of selecting significant factors, detecting outliers in the peer group of buildings, and decomposing changes in energy efficiency into two components (self-efficiency change and peer-efficiency change). To achieve this objective, the following research process was constructed. We devised an approach based on data cloud analysis to detect and remove outlier buildings that affect the DEA benchmarking results significantly. We formulated an innovative DEA benchmark model to calculate the peer-wise, energy-efficiency score of buildings considering the effect of building scales on energy efficiency. We created an energy-efficiency measure based on the Malmquist index to decompose changes in the building energy-efficiency score. We applied the proposed DEA model to benchmark energy efficiency of multifamily properties. The proposed benchmark model is beneficial to building owners and facility managers, because it identifies underperforming buildings and sets these as energy improvement priorities and self-comparable efficiency changes.
A Data Envelopment Analysis Model for Building Energy Efficiency Benchmarking
Building energy benchmarking is required for adopting an energy certification scheme, promoting energy efficiency, and reducing energy consumption. Several methods, such as data envelopment analysis (DEA), were proposed to perform building energy benchmarking. However, existing DEA building energy benchmarking models are subject to several limitations. Current DEA models are sensitive to data outliers such that even a single outlier can result in dramatic changes in efficiency scores of all buildings in a peer group of buildings. In addition, current DEA models cannot determine whether change of the energy-efficiency score of a building is a result of the change of energy efficiency in the building itself or the change of energy efficiency in the peer group of buildings. The objective of this research is to create a peer-wise building energy benchmarking model based on a novel DEA method that is capable of selecting significant factors, detecting outliers in the peer group of buildings, and decomposing changes in energy efficiency into two components (self-efficiency change and peer-efficiency change). To achieve this objective, the following research process was constructed. We devised an approach based on data cloud analysis to detect and remove outlier buildings that affect the DEA benchmarking results significantly. We formulated an innovative DEA benchmark model to calculate the peer-wise, energy-efficiency score of buildings considering the effect of building scales on energy efficiency. We created an energy-efficiency measure based on the Malmquist index to decompose changes in the building energy-efficiency score. We applied the proposed DEA model to benchmark energy efficiency of multifamily properties. The proposed benchmark model is beneficial to building owners and facility managers, because it identifies underperforming buildings and sets these as energy improvement priorities and self-comparable efficiency changes.
A Data Envelopment Analysis Model for Building Energy Efficiency Benchmarking
Lu, Jian (author) / Ashuri, Baabak (author) / Shahandashti, Mohsen (author)
Construction Research Congress 2014 ; 2014 ; Atlanta, Georgia
Construction Research Congress 2014 ; 1073-1082
2014-05-13
Conference paper
Electronic Resource
English
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