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Fitting multiple temporal usage patterns in day-ahead hourly building load forecasting under patch learning framework

  • S.I.: NCAA 2021
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This paper proposes a novel day-ahead hourly building load forecasting approach under the framework of patch learning, a recently proposed data-driven model that aggregates a global model and several patch models to further reduce forecasting errors. A patch learning model based on the long short-term memory network is hereby employed to address such a time-series-based forecasting problem, where the long short-term memory network is considered as the global model and the support vector regression is selected as the patch model. To obtain satisfying performance, the largest absolute error measurement is selected to evaluate load forecasting errors and identify patch locations. Furthermore, a genetic algorithm with an elitist preservation strategy and the grid search method are employed for hyperparameter tuning of the global model and patch models, respectively. The performance of the proposed model is tested and verified on two practical building load data sets and the Lorenz chaotic time-series data and compared with four advanced building load forecasting models on several common metrics.

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Acknowledgements

This work is supported by the National Key Research and Development Program of China (2021YFF0500903), and the National Natural Science Foundation of China (61803162, 52178271, 61873319, 61803054).

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Authors and Affiliations

  1. Key Laboratory of Ministry of Education for Image Processing and Intelligent Control, Artificial Intelligence and Automation School, Huazhong University of Science and Technology, 430074, Wuhan, China

    Zhaohui Dan, Bo Wang, Huijin Fan & Lei Liu

  2. State Grid Taizhou Power Supply Company, 225309, Taizhou, China

    Qian Zhang

  3. School of Automation, Chongqing University, 174 Shazheng Street, Shapingba, 400044, Chongqing, China

    Zhou Wu

  4. Department of Industrial Engineering, Tsinghua University, 100084, Beijing, China

    Muxia Sun

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  1. Zhaohui Dan
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  2. Bo Wang
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  4. Zhou Wu
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Correspondence to Bo Wang.

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We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, and there is no professional or other personal interest of any nature or kind in any product, service, and company that could be construed as influencing the position presented in, or the review of, the manuscript entitled, “Fitting multiple temporal usage patterns in day-ahead hourly building load forecasting under patch learning framework.”

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Dan, Z., Wang, B., Zhang, Q. et al. Fitting multiple temporal usage patterns in day-ahead hourly building load forecasting under patch learning framework. Neural Comput & Applic 34, 16291–16309 (2022). https://doi.org/10.1007/s00521-022-07152-1

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  • DOI: https://doi.org/10.1007/s00521-022-07152-1

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