Optimization of power system load forecasting and scheduling based on artificial neural networks

Optimization of power system load forecasting and scheduling based on artificial neural networks

Abstract This study seeks to enhance the accuracy and economic efficiency of power system load forecasting (PSLF) by leveraging Artificial Neural Networks. A predictive model based on a Residual Connection Bidirectional Long Short Term Memory Attention mechanism (RBiLSTM-AM) is proposed. In this mod...

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Bibliographic Details
Main Authors: Jiangbo Jing, Hongyu Di, Ting Wang, Ning Jiang, Zhaoyang Xiang
Format: Article
Language:English
Published: SpringerOpen 2025-01-01
Series:Energy Informatics
Subjects:
Artificial neural networks
Long short-term memory network
Power system load
Optimal scheduling
Economic efficiency
Online Access:https://doi.org/10.1186/s42162-024-00467-4
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https://doi.org/10.1186/s42162-024-00467-4

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