Abstract
Short-term load forecasting of the power grid can realize the optimal configuration of power generation and dispatch of the power grid which saves energy to the greatest extent and ensures the stable operation of the power system. The power load data is affected by many factors and presents complex volatility. It is difficult for a single prediction method to obtain accurate prediction results. In this paper, a combined optimization prediction method based on Hilbert-Huang transform (HHT) is proposed. By acquiring more regular component sequences of load data, its essential characteristics are explored and then combined with different neural network models for prediction to improve the accuracy and stability of short-term load forecasting. Simulation experiment results verify the prediction accuracy of the combined prediction method.
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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Zhang, Y., Xia, S., Chen, C., Yang, F., He, X. (2023). Combined Short-Term Load Forecasting Method Based on HHT. In: Deng, DJ., Chao, HC., Chen, JC. (eds) Smart Grid and Internet of Things. SGIoT 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 497. Springer, Cham. https://doi.org/10.1007/978-3-031-31275-5_10
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DOI: https://doi.org/10.1007/978-3-031-31275-5_10
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