Research on the Impact of the Differencing Operator on Ensemble Learning Algorithms in the Case of Peak Load Forecasting
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s13369-024-09460-1.pdf
Reference45 articles.
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2. Zhang, Y.; Wen, H.; Wu, Q.; Ai, Q.: Optimal adaptive prediction intervals for electricity load forecasting in distribution systems via reinforcement learning. IEEE Trans. Smart Grid. 14(4), 3259–3270 (2023). https://doi.org/10.1109/TSG.2022.3226423
3. Guo, W.; Che, L.; Shahidehpour, M.; Wan, X.: Machine-Learning based methods in short-term load forecasting. Electr. J. 34(1), 106884 (2021). https://doi.org/10.1016/j.tej.2020.106884
4. Wu, D.; Lin, W.: Efficient residential electric load forecasting via transfer learning and graph neural networks. IEEE Trans. Smart Grid. 14(3), 2423–2431 (2023). https://doi.org/10.1109/TSG.2022.3208211
5. Zawadali, M.; Nasmussakib Khan Shabbir, M.; Sifatulalam Chowdhury, M.; Ghosh, A;, Liang, X.: Regression models of critical parameters affecting peak load demand forecasting. Can Conf Electr Comput Eng. 2018;2018-May:31–34. https://doi.org/10.1109/CCECE.2018.8447786.
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