Research on power load forecasting model based on BILSTM

Jie Kong; Huidong Lei

doi:10.1117/12.3031170

19 July 2024 Research on power load forecasting model based on BILSTM

Jie Kong, Huidong Lei

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131813M (2024) https://doi.org/10.1117/12.3031170
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

The demand side power load in the power system is affected by various factors, and relying on experience to ensure stable power operation is complex and difficult. Only accurate prediction of power load can provide scientific and reasonable scheduling and power generation plans for power system scheduling. In response to the problem of low accuracy in power load forecasting relying on manual experience, this article uses Pearson correlation coefficient to conduct correlation analysis on the influencing factors of power load and establish a power load dataset. On this basis, a convolutional neural network and a bidirectional long short-term memory recurrent neural network are constructed to construct a power load prediction model, and attention mechanism is integrated to use the whale algorithm to search for the optimal parameters that match the network structure. Train the dataset through predictive models to obtain predictive data and achieve power load forecasting. The experimental results show that the constructed power load prediction model can predict power load data, providing scientific basis for guiding power system scheduling.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jie Kong and Huidong Lei "Research on power load forecasting model based on BILSTM", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131813M (19 July 2024); https://doi.org/10.1117/12.3031170

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