Enhancing Short-Term Load Forecasting Accuracy in High-Volatility Regions Using LSTM-SCN Hybrid Models

Enhancing Short-Term Load Forecasting Accuracy in High-Volatility Regions Using LSTM-SCN Hybrid Models

Short-Term Load Forecasting (STLF) is essential for the efficient management of power systems, as it improves forecasting accuracy while optimizing power scheduling efficiency. Despite significant recent advancements in STLF models, forecasting accuracy in high-volatility regions remains a key chall...

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Bibliographic Details
Main Authors: Bingbing Tang, Jie Hu, Mei Yang, Chenglong Zhang, Qiang Bai
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Applied Sciences
Subjects:
Short-Term Load Forecasting
high-volatility regions
Long Short-Term Memory
Stochastic Configuration Network
universal approximation property
Online Access:https://www.mdpi.com/2076-3417/14/24/11606
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