Benchmarking energy performance of residential buildings using two-stage multifactor data envelopment analysis with degree-day based simple-normalization approach

Abstract Being able to identify detailed meta factors of energy performance is essential for creating effective residential energy-retrofitting strategies. Compared to other benchmarking methods, nonparametric multifactor DEA (data envelopment analysis) is capable of discriminating scale factors from management factors to reveal more details to better guide retrofitting practices. A two-stage DEA energy benchmarking method is proposed in this paper. This method includes (1) first-stage meta DEA which integrates the common degree day metrics for neutralizing noise energy effects of exogenous climatic variables; and (2) second-stage Tobit regression for further detailed efficiency analysis. A case study involving 3-year longitudinal panel data of 189 residential buildings indicated the proposed method has advantages over existing methods in terms of its efficiency in data processing and results interpretation. The results of the case study also demonstrated high consistency with existing linear regression based DEA.

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