Multi-Step Ahead Short-Term Load Forecasting Using Hybrid Feature Selection and Improved Long Short-Term Memory Network

Multi-Step Ahead Short-Term Load Forecasting Using Hybrid Feature Selection and Improved Long Short-Term Memory Network

Short-term load forecasting (STLF) plays an important role in the economic dispatch of power systems. Obtaining accurate short-term load can greatly improve the safety and economy of a power grid operation. In recent years, a large number of short-term load forecasting methods have been proposed. Ho...

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Bibliographic Details
Main Authors: Shaoqian Pei, Hui Qin, Liqiang Yao, Yongqi Liu, Chao Wang, Jianzhong Zhou
Format: Article
Language:English
Published: MDPI AG 2020-08-01
Series:Energies
Subjects:
short-term load forecasting
Max-Relevance and Min-Redundancy
Improved Long Short-Term Memory network
multi-step ahead load
hybrid feature selection
Online Access:https://www.mdpi.com/1996-1073/13/16/4121

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https://www.mdpi.com/1996-1073/13/16/4121

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