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Short-term forecasting electricity load by long short-term memory and reinforcement learning for optimization of hyper-parameters

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Abstract

Electricity load forecasting is an essential operation of the power system. Deep learning is used to improve accurate electricity load forecasting. In this study, combining Long short-term memory and reinforcement learning are proposed to encourage the advantage of a single approach for forecasting. Importance input features, including the mutual feature of electricity load, are used to increase accuracy. First, multi-time series input can handle by Long short-term memory and the addition of features supports to the load feature will make the model better efficient. Because the LSTM model is quite complex, choosing a good set of hyperparameters is difficult. Therefore, the purpose of using reinforcement learning is to optimize hyper-parameters of the Long short-term memory model. The proposed model is the combination of Long-short term memory and reinforcement learning. The proposed model will be applied in two electricity load data sets, the real-life data of Vietnam Electricity and the other public data set. In one day ahead forecasting, the proposed model archives superior performance than the benchmark.

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Data availability

Data is available on https://github.com/anhnguyenthingoc/Forecasting-Electricity-Load.

Code availability

Not applicable.

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Funding

This research was supported by the Vietnam Ministry of Education and Training [grant number B2023-BKA-07].

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Authors and Affiliations

  1. School of Applied Mathematics and Informatics, Hanoi University of Science and Technology, Hanoi, Vietnam

    Ngoc Anh Nguyen & Tien Dat Dang

  2. School of Engineering and Technology, Universidad Internacional de La Rioja, Logroño, Spain

    Elena Verdú

  3. Department of Computer Science and Engineering, CMR Institute of Technology, Hyderabad, India

    Vijender Kumar Solanki

Authors
  1. Ngoc Anh Nguyen
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  2. Tien Dat Dang
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  3. Elena Verdú
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  4. Vijender Kumar Solanki
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Contributions

The authors confirm contribution to the paper as follows: (i) Ngoc Anh Nguyen developed the theoretical framework study, proposed conception and design,collected data, wrote draft manuscript preparation; (ii) Tien Dat Dang proposed conception and design, analysed and interpreted of results, wrote draft manuscript preparation; (iii) Elena Verdú wrote draft manuscript preparation,reviewed the results and approved the final version of the manuscript; (iv) Vijender Kumar Solanki wrote draft manuscript preparation,reviewed the results and approved the final version of the manuscript.

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Correspondence to Ngoc Anh Nguyen.

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Nguyen, N.A., Dang, T.D., Verdú, E. et al. Short-term forecasting electricity load by long short-term memory and reinforcement learning for optimization of hyper-parameters. Evol. Intel. 16, 1729–1746 (2023). https://doi.org/10.1007/s12065-023-00869-5

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