Version 1
: Received: 3 July 2024 / Approved: 4 July 2024 / Online: 4 July 2024 (11:18:24 CEST)
How to cite:
Xu, M.; Liu, W.; Wang, S.; Tian, J.; Wu, P.; Xie, C. 24-Step Short-term Power Load Forecasting Model Utilizing KOA-BiTCN-BiGRU-Attentions. Preprints2024, 2024070391. https://doi.org/10.20944/preprints202407.0391.v1
Xu, M.; Liu, W.; Wang, S.; Tian, J.; Wu, P.; Xie, C. 24-Step Short-term Power Load Forecasting Model Utilizing KOA-BiTCN-BiGRU-Attentions. Preprints 2024, 2024070391. https://doi.org/10.20944/preprints202407.0391.v1
Xu, M.; Liu, W.; Wang, S.; Tian, J.; Wu, P.; Xie, C. 24-Step Short-term Power Load Forecasting Model Utilizing KOA-BiTCN-BiGRU-Attentions. Preprints2024, 2024070391. https://doi.org/10.20944/preprints202407.0391.v1
APA Style
Xu, M., Liu, W., Wang, S., Tian, J., Wu, P., & Xie, C. (2024). 24-Step Short-term Power Load Forecasting Model Utilizing KOA-BiTCN-BiGRU-Attentions. Preprints. https://doi.org/10.20944/preprints202407.0391.v1
Chicago/Turabian Style
Xu, M., Peng Wu and Congjiu Xie. 2024 "24-Step Short-term Power Load Forecasting Model Utilizing KOA-BiTCN-BiGRU-Attentions" Preprints. https://doi.org/10.20944/preprints202407.0391.v1
Abstract
With the global objectives of achieving a ‘carbon peak’ and ‘carbon neutrality’, along with the implementation of carbon reduction policies, China’s industrial structure has undergone significant adjustments, resulting in constraints on high-energy consumption and high-emission industries while promoting the rapid growth of green industries. Consequently, these changes have led to an increasingly complex power system structure and presented new challenges for electricity demand forecasting. To address this issue, this study proposes a 24-step multivariate time series short-term load forecasting algorithm model based on KNN data imputation and BiTCN bidirectional temporal convolutional networks combined with BiGRU bidirectional gated recurrent units and attention mechanism. The Kepler adaptive optimization algorithm (KOA) is employed for hyperparameter optimization to effectively enhance prediction accuracy. Furthermore, using real load data from a wind farm in Xinjiang as an example, this paper predicts the electricity load from January 1st to December 30th in 2019. Experimental results demonstrate that our comprehensive short-term load forecasting model exhibits lower prediction errors and superior performance compared to traditional methods, thus holding great value for practical applications.
Computer Science and Mathematics, Computational Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
