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Deep reservoir architecture for short-term residential load forecasting: An online learning scheme for edge computing

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  • Fujimoto, Yu
  • Fujita, Megumi
  • Hayashi, Yasuhiro

Abstract

The short-term residential electricity demand forecast tasks are essential for designing the portfolio of the decision-making process of aggregators for the selection of control targets and derivation of specific control target values for demand response. The implementation of edge computing on the forecast functionality, which provides forecasting and minimal communication functions based on the data accumulated at the end-user-side using an inexpensive computational resource deployed locally, is an attractive framework for practical implementation. To achieve good accuracy while forecasting the demands of individual households during practical long-term operation, it is necessary to follow the changes in demand characteristics quickly by updating the prediction models frequently. This study focused on the concept of deep reservoir computing, which has a high affinity for implementation in edge computing frameworks from the viewpoint of hardware implementability, while utilizing the deep architecture and avoiding high computational cost. In particular, this study proposed an online learning scheme for the implementation of edge computing, which does not require large-scale computation for updating the prediction models. The effectiveness of the proposed approach was evaluated considering the calculation cost and accuracy through numerical experiments using real-world residential load data.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:298:y:2021:i:c:s0306261921006061
    DOI: 10.1016/j.apenergy.2021.117176
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    References listed on IDEAS

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    Cited by:

    1. Yu Fujimoto & Akihisa Kaneko & Yutaka Iino & Hideo Ishii & Yasuhiro Hayashi, 2023. "Challenges in Smartizing Operational Management of Functionally-Smart Inverters for Distributed Energy Resources: A Review on Machine Learning Aspects," Energies, MDPI, vol. 16(3), pages 1-26, January.
    2. 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.
    3. Eren, Yavuz & Küçükdemiral, İbrahim, 2024. "A comprehensive review on deep learning approaches for short-term load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).

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