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Review of Family-Level Short-Term Load Forecasting and Its Application in Household Energy Management System

Author

Listed:
  • Ping Ma

    (School of Electrical Engineering, Weihai Innovation Research Institute, Qingdao University, Qingdao 266000, China
    These authors contributed equally to this work.)

  • Shuhui Cui

    (School of Electrical Engineering, Weihai Innovation Research Institute, Qingdao University, Qingdao 266000, China
    These authors contributed equally to this work.)

  • Mingshuai Chen

    (Rizhao Power Supply Company, State Grid Shandong Electric Power Company, Rizhao 276826, China)

  • Shengzhe Zhou

    (Department of Information Engineering, Shandong Water Conservancy Vocational College, Rizhao 276826, China)

  • Kai Wang

    (School of Electrical Engineering, Weihai Innovation Research Institute, Qingdao University, Qingdao 266000, China)

Abstract

With the rapid development of smart grids and distributed energy sources, the home energy management system (HEMS) is becoming a hot topic of research as a hub for connecting customers and utilities for energy visualization. Accurate forecasting of future short-term residential electricity demand for each major appliance is a key part of the energy management system. This paper aims to explore the current research status of household-level short-term load forecasting, summarize the advantages and disadvantages of various forecasting methods, and provide research ideas for short-term household load forecasting and household energy management. Firstly, the paper analyzes the latest research results and research trends in deep learning load forecasting methods in terms of network models, feature extraction, and adaptive learning; secondly, it points out the importance of combining probabilistic forecasting methods that take into account load uncertainty with deep learning techniques; and further explores the implications and methods for device-level as well as ultra-short-term load forecasting. In addition, the paper also analyzes the importance of short-term household load forecasting for the scheduling of electricity consumption in household energy management systems. Finally, the paper points out the problems in the current research and proposes suggestions for future development of short-term household load forecasting.

Suggested Citation

  • Ping Ma & Shuhui Cui & Mingshuai Chen & Shengzhe Zhou & Kai Wang, 2023. "Review of Family-Level Short-Term Load Forecasting and Its Application in Household Energy Management System," Energies, MDPI, vol. 16(15), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5809-:d:1210742
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    References listed on IDEAS

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    1. Zhenhua Cui & Jiyong Dai & Jianrui Sun & Dezhi Li & Licheng Wang & Kai Wang & A. M. Bastos Pereira, 2022. "Hybrid Methods Using Neural Network and Kalman Filter for the State of Charge Estimation of Lithium-Ion Battery," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-11, May.
    2. Ning Ma & Huaixian Yin & Kai Wang, 2023. "Prediction of the Remaining Useful Life of Supercapacitors at Different Temperatures Based on Improved Long Short-Term Memory," Energies, MDPI, vol. 16(14), pages 1-14, July.
    3. Zhao, Xueyuan & Gao, Weijun & Qian, Fanyue & Ge, Jian, 2021. "Electricity cost comparison of dynamic pricing model based on load forecasting in home energy management system," Energy, Elsevier, vol. 229(C).
    4. Bingtuan Gao & Xiaofeng Liu & Zhenyu Zhu, 2018. "A Bottom-Up Model for Household Load Profile Based on the Consumption Behavior of Residents," Energies, MDPI, vol. 11(8), pages 1-16, August.
    5. Fekri, Mohammad Navid & Patel, Harsh & Grolinger, Katarina & Sharma, Vinay, 2021. "Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network," Applied Energy, Elsevier, vol. 282(PA).
    6. Shree Krishna Acharya & Young-Min Wi & Jaehee Lee, 2019. "Short-Term Load Forecasting for a Single Household Based on Convolution Neural Networks Using Data Augmentation," Energies, MDPI, vol. 12(18), pages 1-19, September.
    7. Yang, Wangwang & Shi, Jing & Li, Shujian & Song, Zhaofang & Zhang, Zitong & Chen, Zexu, 2022. "A combined deep learning load forecasting model of single household resident user considering multi-time scale electricity consumption behavior," Applied Energy, Elsevier, vol. 307(C).
    8. Zheng, Zhuang & Chen, Hainan & Luo, Xiaowei, 2019. "A Kalman filter-based bottom-up approach for household short-term load forecast," Applied Energy, Elsevier, vol. 250(C), pages 882-894.
    9. Li, Lechen & Meinrenken, Christoph J. & Modi, Vijay & Culligan, Patricia J., 2021. "Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features," Applied Energy, Elsevier, vol. 287(C).
    10. Brusaferri, Alessandro & Matteucci, Matteo & Spinelli, Stefano & Vitali, Andrea, 2022. "Probabilistic electric load forecasting through Bayesian Mixture Density Networks," Applied Energy, Elsevier, vol. 309(C).
    11. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    12. Tingting Hou & Rengcun Fang & Jinrui Tang & Ganheng Ge & Dongjun Yang & Jianchao Liu & Wei Zhang, 2021. "A Novel Short-Term Residential Electric Load Forecasting Method Based on Adaptive Load Aggregation and Deep Learning Algorithms," Energies, MDPI, vol. 14(22), pages 1-21, November.
    13. Lusis, Peter & Khalilpour, Kaveh Rajab & Andrew, Lachlan & Liebman, Ariel, 2017. "Short-term residential load forecasting: Impact of calendar effects and forecast granularity," Applied Energy, Elsevier, vol. 205(C), pages 654-669.
    14. Fujimoto, Yu & Fujita, Megumi & Hayashi, Yasuhiro, 2021. "Deep reservoir architecture for short-term residential load forecasting: An online learning scheme for edge computing," Applied Energy, Elsevier, vol. 298(C).
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