A LSTM-based time series prediction model for China's power supply and its performance evaluation

Authors

  • Zhihao Zhang

DOI:

https://doi.org/10.54097/3gbp7648

Keywords:

Long short-term memory network (LSTM), Power supply, Time series prediction, Data preprocessing, Performance evaluation

Abstract

With the rapid development of China's economy and the acceleration of urbanisation, the stability and sustainability of power supply has become increasingly important. In order to cope with the continuous growth of power demand, accurate power supply prediction has become an essential research direction. In this paper, a time series prediction model for power supply is constructed based on the long short-term memory network (LSTM). Power supply data is organised using methods such as data pre-processing and correlation analysis, and the LSTM model is used to predict future power supply. The performance of the prediction results was evaluated, and the results showed that the model can effectively capture the changing trends of power supply, with high reliability and accuracy, providing important support for scientific management and policy formulation in the power industry.

References

[1] Kumar, I., Tripathi, B. K., & Singh, A. (2023). Attention-based LSTM network-assisted time series forecasting models for petroleum production. Engineering Applications of Artificial Intelligence, 123, 106440.

[2] Li, K., Huang, W., Hu, G., & Li, J. (2023). Ultra-short term power load forecasting based on CEEMDAN-SE and LSTM neural network. Energy and Buildings, 279, 112666.

[3] Guo, H., Chen, Q., Zheng, K., Xia, Q., & Kang, C. (2021). Forecast aggregated supply curves in power markets based on LSTM model. IEEE Transactions on power systems, 36(6), 5767-5779.

[4] Bulut, M. (2021). Hydroelectric generation forecasting with long short term memory (LSTM) based deep learning model for turkey. arXiv preprint arXiv:2109.09013.

[5] Han, H., Liu, H., Zuo, X., Shi, G., Sun, Y., Liu, Z., & Su, M. (2022). Optimal sizing considering power uncertainty and power supply reliability based on LSTM and MOPSO for SWPBMs. IEEE Systems Journal, 16(3), 4013-4023.

[6] Jailani, N. L. M., Dhanasegaran, J. K., Alkawsi, G., Alkahtani, A. A., Phing, C. C., Baashar, Y., ... & Tiong, S. K. (2023). Investigating the power of LSTM-based models in solar energy forecasting. Processes, 11(5), 1382.

[7] Wang, D., Gan, J., Mao, J., Chen, F., & Yu, L. (2023). Forecasting power demand in China with a CNN-LSTM model including multimodal information. Energy, 263, 126012.

[8] Chen, Y., Cui, S., Chen, P., Yuan, Q., Kang, P., & Zhu, L. (2021). An LSTM-based neural network method of particulate pollution forecast in China. Environmental Research Letters, 16(4), 044006.

[9] Branco, N. W., Cavalca, M. S. M., Stefenon, S. F., & Leithardt, V. R. Q. (2022). Wavelet LSTM for fault forecasting in electrical power grids. Sensors, 22(21), 8323.

[10] Li, Q., Yang, Y., Yang, L., & Wang, Y. (2023). Comparative analysis of water quality prediction performance based on LSTM in the Haihe River Basin, China. Environmental Science and Pollution Research, 30(3), 7498-7509.

[11] He, Y., & Tsang, K. F. (2021). Universities power energy management: A novel hybrid model based on iCEEMDAN and Bayesian optimized LSTM. Energy Reports, 7, 6473-6488.

[12] Li, Y., Tong, Z., Tong, S., & Westerdahl, D. (2022). A data-driven interval forecasting model for building energy prediction using attention-based LSTM and fuzzy information granulation. Sustainable Cities and Society, 76, 103481.

Downloads

Published

22-10-2024

Issue

Section

Articles

How to Cite

Zhang, Z. (2024). A LSTM-based time series prediction model for China’s power supply and its performance evaluation. Journal of Computing and Electronic Information Management, 14(3), 1-6. https://doi.org/10.54097/3gbp7648