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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References
Yao, J.G., Yang, S.C., Gao, Z.H., et al.: Development trend prospects of power dispatching automation system. Autom. Electr. Power Syst. 31(13), 7–11 (2007)
Osman, Z.H., Awad, M.L., Mahmoud, T.K.: Neural network based approach for short-term load forecasting. In: 2009 IEEE/PES Power Systems Conference and Exposition, pp. 1–8. Seattle, WA (2009)
Lu, J.X., Zhang, Q.P., Yang, Z.H., et al.: Short-term load forecasting method based on CNN-LSTM hybrid neural network model. Autom. Electr. Power Syst. 43(8), 131–137 (2019)
Wu, L.Z., Kong, C., Hao, X.H., et al.: A short-term load forecasting method based on GRU-CNN hybrid neural network model. Math. Probl. Eng. 2020, 1–10 (2020)
Liang, Z., Sun, G.Q., Li, H.C., et al.: Short-term load forecasting based on VMD and PSO optimized deep belief network. Power Syst. Technol. 42(2), 598–606 (2018)
Kong, X.Y., Li, C., Zheng, F., et al.: Short-term load forecasting method based on empirical mode decomposition and feature correlation analysis. Autom. Electr. Power Syst. 43(5), 46–51 (2019)
Huang, N.E., Shen, Z., Long, S.R., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, pp. 903–995 (1998)
Zhu, Z.H., Sun, Y.L., Ji, Y.: Short-term load forecasting based on EMD and SVM. High Volt. Eng. 33(5), 118–122 (2007)
Huang, C.G.: Hilbert transform and its applications. J. Chengdu Inst. Meteorol. 14(3), 273–276 (1999)
Munir, B.S., Reza, M., Trisetyarso, A., et al.: Feature extraction using Hilbert-Huang transform for power system oscillation measurements. In: 2017 4th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), pp. 93–96. Semarang, Indonesia (2017)
Schilling, R.J., Carroll, J.J., Al-Ajlouni, A.F.: Approximation of nonlinear systems with radial basis function neural networks. IEEE Trans. Neural Netw. 12(1), 1–15 (2001)
Gers, F.A., Schmidhuber, J., Cummins F.: Learning to forget: continual prediction with LSTM. In: Ninth International Conference on Artificial Neural Networks (ICANN), pp. 850–855. Edinburgh, UK (1999)
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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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