Multi-node short-term power load forecasting model based on neural network ensemble

Jinyue Qian; Yifan Sun; Jia Wu; Bailang Pan; Qiuqiang Zhou; Liwu Gong

doi:10.1117/12.3012103

21 December 2023 Multi-node short-term power load forecasting model based on neural network ensemble

Jinyue Qian, Yifan Sun, Jia Wu, Bailang Pan, Qiuqiang Zhou, Liwu Gong

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 129700C (2023) https://doi.org/10.1117/12.3012103
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

High-precision load forecasting is a prerequisite for ensuring the safety and stability of power systems. In order to solve the problem of the insufficient generalization ability of a single model, this paper proposes a deep temporal neural network ensemble model based on multi-node short-term load forecasting. It can effectively reduce the prediction error, improve the prediction accuracy and shorten the prediction time to ensure the stable operation of the system. It can also improve the efficiency of clean energy utilization and optimize the level of resource allocation.

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

Citation Download Citation

Jinyue Qian, Yifan Sun, Jia Wu, Bailang Pan, Qiuqiang Zhou, and Liwu Gong "Multi-node short-term power load forecasting model based on neural network ensemble", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129700C (21 December 2023); https://doi.org/10.1117/12.3012103

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