Short-term power load forecasting based on multi-step probability distribution deep learning network

Ming Chen; Huaijin Gao; Kun Chen; Chong Du; Zhijie Yang

doi:10.1117/12.3004131

11 October 2023 Short-term power load forecasting based on multi-step probability distribution deep learning network

Ming Chen, Huaijin Gao, Kun Chen, Chong Du, Zhijie Yang

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128001Z (2023) https://doi.org/10.1117/12.3004131
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

With the improvement of renewable energy utilization rate, its proportion in power generation continues to increase. However, the uncertainty of renewable energy power generation has always been an urgent problem. Accurate forecast of short-term power load can assist modern power systems in accurately allocating the proportion of traditional and renewable energy generation. This is crucial for the safety and economic operation of the power grid. Power load forecasting can be seen as a multivariate time series forecasting problem. Most of existing studies have focused on predicting the next moment power load. However, a great number of decisions in power planning scenarios require predictive models that can provide a complete conditional distribution with richer information, namely probability distribution prediction. To address this limitation, we applied a multi-step probability prediction model based on recurrent neural networks on short-term power load forecasting. Experiments conducted on the annual power load of multiple regions in the United States and comparison with some classic models verify the superiority of the applied model. The results indicate that the model applied in the article has a promising predictive performance.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ming Chen, Huaijin Gao, Kun Chen, Chong Du, and Zhijie Yang "Short-term power load forecasting based on multi-step probability distribution deep learning network", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128001Z (11 October 2023); https://doi.org/10.1117/12.3004131

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