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Clustering-based short-term load forecasting for residential electricity under the increasing-block pricing tariffs in China

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  • Fu, Xin
  • Zeng, Xiao-Jun
  • Feng, Pengpeng
  • Cai, Xiuwen

Abstract

The introduction of a new pricing mechanism, the increasing-block tariff (IBT), will not only affect electricity bills for residents, but also lead to a change in residential electricity consumption behaviours. Understanding these consumption patterns will help create more accurate load forecasting and increase the efficiency of the IBT. This study proposes an innovative clustering-based approach for short-term load forecasting under the IBT in China. The new approach initially partitions households into homogeneous groups each of which has distinctive consumption patterns under the IBT, each consumer segment can then select the most appropriate model for load forecasting, and the predicted load demands of different clusters are aggregated to derive the total usage. In particular, the IBT-related attributes are newly introduced into the clustering analysis. The utility and effectiveness of the proposed model is confirmed through a realistic dataset that contains the daily household-level consumption data of 533 households from April 2014 to February 2015. Consequently, the households are classified into five clusters with distinctive consumption patterns, including low-demand and insensitivity to high temperature (Cluster1), ordinary users and sensitivity to high temperature (Cluster2), ordinary users and sensitivity to the IBT (Cluster3), high-demand consumers and sensitivity to high temperature (Cluster4), and luxury consumers (Cluster5). In addition, the obtained experimental results demonstrate that the proposed approach can not only achieve better prediction accuracy (e.g., the mean absolute percentage error (MAPE) improves from 3.82% to 2.28% by using autoregressive integrated moving average (ARIMA)), but also provide better flexibility for hybrid modelling. From the practical implication point of view, the proposed forecasting model can help power companies to provide a reliable and high-quality electricity supply as well as to establish appropriate schedules of operations and maintenance within a certain area. Moreover, the identified consumption behaviours can be analysed and used to improve the design and promote awareness/acceptance of the IBT.

Suggested Citation

  • Fu, Xin & Zeng, Xiao-Jun & Feng, Pengpeng & Cai, Xiuwen, 2018. "Clustering-based short-term load forecasting for residential electricity under the increasing-block pricing tariffs in China," Energy, Elsevier, vol. 165(PB), pages 76-89.
    • Handle: RePEc:eee:energy:v:165:y:2018:i:pb:p:76-89
    DOI: 10.1016/j.energy.2018.09.156
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    2. Ming Meng & Shucheng Wu & Jin Zhou & Xinfang Wang, 2019. "What is Currently Driving the Growth of China’s Household Electricity Consumption? A Clustering and Decomposition Analysis," Sustainability, MDPI, vol. 11(17), pages 1-14, August.
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    5. Tao, Peng & Xu, Fei & Dong, Zengbo & Zhang, Chao & Peng, Xuefeng & Zhao, Junpeng & Li, Kangping & Wang, Fei, 2022. "Graph convolutional network-based aggregated demand response baseline load estimation," Energy, Elsevier, vol. 251(C).
    6. Kang, J. & Reiner, D., 2021. "Identifying residential consumption patterns using data-mining techniques: A large-scale study of smart meter data in Chengdu, China," Cambridge Working Papers in Economics 2143, Faculty of Economics, University of Cambridge.
    7. Lin, Boqiang & Wang, Yao, 2020. "Analyzing the elasticity and subsidy to reform the residential electricity tariffs in China," International Review of Economics & Finance, Elsevier, vol. 67(C), pages 189-206.
    8. Ayertey, Winfred & Sharifi, Ayyoob & Yoshida, Yuichiro, 2024. "The impact of increase in block pricing on electricity demand responsiveness: Evidence from Ghana," Energy, Elsevier, vol. 288(C).
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