•  
  •  
 
Building Simulation: An International Journal

Article Title

Cluster analysis for occupant-behavior based electricity load patterns in buildings: A case study in Shanghai residences

Authors

Song Pan, Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing 100124, China
Xinru Wang, Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing 100124, China
Yixuan Wei, Research Centre for Fluids and Thermal Engineering, University of Nottingham Ningbo China, Ningbo 315100, China
Xingxing Zhang, Department of Energy, Forests and Built Environments, Dalarna University, Falun, 79188, Sweden
Csilla Gal, Department of Energy, Forests and Built Environments, Dalarna University, Falun, 79188, Sweden
Guangying Ren, Department of Architecture, University of Cambridge, Cambridge, CB2 1PX, UK
Da Yan, Building Energy Conservation Research Center, Tsinghua University, Beijing 100084, China
Yong Shi, Research Centre for Fluids and Thermal Engineering, University of Nottingham Ningbo China, Ningbo 315100, China
Jinshun Wu, Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing 100124, China
Liang Xia, Research Centre for Fluids and Thermal Engineering, University of Nottingham Ningbo China, Ningbo 315100, China
Jingchao Xie, Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing 100124, China
Jiaping Liu, Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing 100124, China

Keywords

occupancy behavior, K-means cluster, electricity, load profile, residential building

Abstract

In building performance simulation, occupant behavior contributes to large uncertainties, which often lead to considerable discrepancies between actual energy consumption and simulation results. This paper aims to extract occupant-behavior related electricity load patterns using classical K-means clustering approach at the initial investigation stage. Smart-metering data from a case study in Shanghai, China, was used for the load pattern analysis. The electricity load patterns of occupants were examined on a daily/weekly/seasonal basis. According to their load patterns, occupants were categorized as (a) white-collar workers, (b) poor or older families and (c) rich or young families. The daily patterns indicated that electricity use was much more random and fluctuated over a wide range. Most households of the monitored communities consumed relatively-low electricity; the characteristic double peak with higher level of consumption in the morning and evening were only apparent in a relatively small subset of residents (mostly white-collar workers). The weekly analysis found that significant load shifting towards weekend days occurred in the poor or old family group. The electricity saving potential was greatest in the white-collar workers and the rich or young family groups. This study concludes with recommendations to stakeholders utilizing our load profiling results. The research provides a rare insight into the electricity-use-related occupant behaviors of Shanghai residents through the case study of two communities. The findings of the study are also presented in a meaningful way so that they can directly aid the decision-making of governments and other stakeholders interested in energy efficiency. The research results are also relevant to the building energy simulation community as they are derived from observations, and thus can have the potential to improve the efficiency and accuracy of numerical simulation results.

Publisher

Tsinghua University Press

Link to Full Text
Request a Copy

Share

COinS