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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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    as
    1. Hung, Ming-Feng & Huang, Tai-Hsin, 2015. "Dynamic demand for residential electricity in Taiwan under seasonality and increasing-block pricing," Energy Economics, Elsevier, vol. 48(C), pages 168-177.
    2. Zhang, Lei & Wu, Yang, 2012. "Market segmentation and willingness to pay for green electricity among urban residents in China: The case of Jiangsu Province," Energy Policy, Elsevier, vol. 51(C), pages 514-523.
    3. Lin, Boqiang & Jiang, Zhujun, 2011. "Estimates of energy subsidies in China and impact of energy subsidy reform," Energy Economics, Elsevier, vol. 33(2), pages 273-283, March.
    4. Silva, Susana & Soares, Isabel & Pinho, Carlos, 2018. "Electricity residential demand elasticities: Urban versus rural areas in Portugal," Energy, Elsevier, vol. 144(C), pages 627-632.
    5. Liu, Yang & Wang, Wei & Ghadimi, Noradin, 2017. "Electricity load forecasting by an improved forecast engine for building level consumers," Energy, Elsevier, vol. 139(C), pages 18-30.
    6. Hyland, Marie & Leahy, Eimear & Tol, Richard S.J., 2013. "The potential for segmentation of the retail market for electricity in Ireland," Energy Policy, Elsevier, vol. 61(C), pages 349-359.
    7. Wang, Zhaohua & Zhang, Bin & Zhang, Yixiang, 2012. "Determinants of public acceptance of tiered electricity price reform in China: Evidence from four urban cities," Applied Energy, Elsevier, vol. 91(1), pages 235-244.
    8. Lin, Boqiang & Jiang, Zhujun, 2012. "Designation and influence of household increasing block electricity tariffs in China," Energy Policy, Elsevier, vol. 42(C), pages 164-173.
    9. Li, Song & Goel, Lalit & Wang, Peng, 2016. "An ensemble approach for short-term load forecasting by extreme learning machine," Applied Energy, Elsevier, vol. 170(C), pages 22-29.
    10. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
    11. Okajima, Shigeharu & Okajima, Hiroko, 2013. "Estimation of Japanese price elasticities of residential electricity demand, 1990–2007," Energy Economics, Elsevier, vol. 40(C), pages 433-440.
    12. Räsänen, Teemu & Voukantsis, Dimitrios & Niska, Harri & Karatzas, Kostas & Kolehmainen, Mikko, 2010. "Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data," Applied Energy, Elsevier, vol. 87(11), pages 3538-3545, November.
    13. Zhang, Jinliang & Wei, Yi-Ming & Li, Dezhi & Tan, Zhongfu & Zhou, Jianhua, 2018. "Short term electricity load forecasting using a hybrid model," Energy, Elsevier, vol. 158(C), pages 774-781.
    14. Wang, Siyan & Sun, Xun & Lall, Upmanu, 2017. "A hierarchical Bayesian regression model for predicting summer residential electricity demand across the U.S.A," Energy, Elsevier, vol. 140(P1), pages 601-611.
    15. Liu, Nian & Tang, Qingfeng & Zhang, Jianhua & Fan, Wei & Liu, Jie, 2014. "A hybrid forecasting model with parameter optimization for short-term load forecasting of micro-grids," Applied Energy, Elsevier, vol. 129(C), pages 336-345.
    16. Sun, Chuanwang & Lin, Boqiang, 2013. "Reforming residential electricity tariff in China: Block tariffs pricing approach," Energy Policy, Elsevier, vol. 60(C), pages 741-752.
    17. Moral-Carcedo, Julián & Pérez-García, Julián, 2017. "Integrating long-term economic scenarios into peak load forecasting: An application to Spain," Energy, Elsevier, vol. 140(P1), pages 682-695.
    18. Lin, Boqiang & Liu, Xia, 2013. "Electricity tariff reform and rebound effect of residential electricity consumption in China," Energy, Elsevier, vol. 59(C), pages 240-247.
    19. Dong, Xiao-Ying & Hao, Yu, 2018. "Would income inequality affect electricity consumption? Evidence from China," Energy, Elsevier, vol. 142(C), pages 215-227.
    20. Chicco, Gianfranco, 2012. "Overview and performance assessment of the clustering methods for electrical load pattern grouping," Energy, Elsevier, vol. 42(1), pages 68-80.
    21. Che, JinXing & Wang, JianZhou, 2014. "Short-term load forecasting using a kernel-based support vector regression combination model," Applied Energy, Elsevier, vol. 132(C), pages 602-609.
    22. Chahkoutahi, Fatemeh & Khashei, Mehdi, 2017. "A seasonal direct optimal hybrid model of computational intelligence and soft computing techniques for electricity load forecasting," Energy, Elsevier, vol. 140(P1), pages 988-1004.
    23. Prasanna, Ashreeta & Mahmoodi, Jasmin & Brosch, Tobias & Patel, Martin K., 2018. "Recent experiences with tariffs for saving electricity in households," Energy Policy, Elsevier, vol. 115(C), pages 514-522.
    24. Nowotarski, Jakub & Liu, Bidong & Weron, Rafał & Hong, Tao, 2016. "Improving short term load forecast accuracy via combining sister forecasts," Energy, Elsevier, vol. 98(C), pages 40-49.
    25. Peter C. Reiss & Matthew W. White, 2005. "Household Electricity Demand, Revisited," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 853-883.
    26. de Oliveira, Erick Meira & Cyrino Oliveira, Fernando Luiz, 2018. "Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods," Energy, Elsevier, vol. 144(C), pages 776-788.
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    8. Haben, Stephen & Arora, Siddharth & Giasemidis, Georgios & Voss, Marcus & Vukadinović Greetham, Danica, 2021. "Review of low voltage load forecasting: Methods, applications, and recommendations," Applied Energy, Elsevier, vol. 304(C).

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