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Power Load Forecast Based on CS-LSTM Neural Network

Author

Listed:
  • Lijia Han

    (Institute of Information and Computation, School of Mathematics and Physics, North China Electric Power University, Beijing 102206, China)

  • Xiaohong Wang

    (Institute of Information and Computation, School of Mathematics and Physics, North China Electric Power University, Beijing 102206, China)

  • Yin Yu

    (Nanjing Laisi Information Technology Co., Ltd., Nanjing 210014, China)

  • Duan Wang

    (Department of Mathematics, Nuclear Industry College, Beijing 102413, China)

Abstract

Load forecast is the foundation of power system operation and planning. The forecast results can guide the power system economic dispatch and security analysis. In order to improve the accuracy of load forecast, this paper proposes a forecasting model based on the combination of the cuckoo search (CS) algorithm and the long short-term memory (LSTM) neural network. Load data are specific data with time series characteristics and periodicity, and the LSTM algorithm can control the information added or discarded through the forgetting gate, so as to realize the function of forgetting or memorizing. Therefore, the use of the LSTM algorithm for load forecast is more effective. The CS algorithm can perform global search better and does not easily fall into local optima. The CS-LSTM forecasting model, where CS algorithm is used to optimize the hyper-parameters of the LSTM model, has a better forecasting effect and is more feasible. Simulation results show that the CS-LSTM model has higher forecasting accuracy than the standard LSTM model, the PSO-LSTM model, and the GA-LSTM model.

Suggested Citation

  • Lijia Han & Xiaohong Wang & Yin Yu & Duan Wang, 2024. "Power Load Forecast Based on CS-LSTM Neural Network," Mathematics, MDPI, vol. 12(9), pages 1-17, May.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:9:p:1402-:d:1388268
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    References listed on IDEAS

    as
    1. Hong, Wei-Chiang, 2010. "Application of chaotic ant swarm optimization in electric load forecasting," Energy Policy, Elsevier, vol. 38(10), pages 5830-5839, October.
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