Using climate classification to evaluate building energy performance
Resource
ENERGY, 36(3), 1797-1801
Journal
Energy
Pages
1797-1801
Date Issued
2011
Date
2011
Author(s)
Lee, Wen-Shing
Kung, Chung-Kuan
Abstract
Traditional benchmarking of building energy performance usually starts by considering a wide range of different factors and giving these factors different weights to help reach one general indicator measuring a building's overall energy performance. For obtaining more specific information in building energy management performance, this paper proposes an adjustment to the traditional approach by using climate classification and data envelopment analysis (DEA). The study first adopts cluster analysis to classify the evaluated buildings into different climate clusters. Secondly, scale factors are identified by regression analysis. DEA is then employed to assess the energy management efficiency of the evaluated buildings. The samples of 122 office buildings in Taiwan in summer are classified into three climate clusters (warm and long rain hour, hot and middle rain hour, and hot and short rain hour). Research results indicate that the average indicators of energy management performance in each of the three climate clusters are 0.5, 0.56, and 0.56 respectively. The lower value indicator of energy management performance, resulted from the comparison between the energy consumption of the evaluated building and the minimum energy consumption among buildings in the same scale and similar climate conditions, indicates a more potential in energy saving. © 2010 Elsevier Ltd.
Subjects
Climate classification; Cluster analysis; Data envelopment analysis; Energy management
Other Subjects
Cluster analysis; Data envelopment analysis; Energy efficiency; Energy utilization; Management; Office buildings; Rain; Rating; Regression analysis; Building energy performance; Climate classification; Climate condition; Energy consumption; Energy performance; Energy saving; In-buildings; Management efficiency; Minimum energy; Research results; Scale Factor; Specific information; Energy conservation; benchmarking; building; climate classification; cluster analysis; energy planning; energy use; performance assessment; regression analysis; sampling; Taiwan
Type
journal article
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