Short Term Electrical Load Forecasting Using Mutual Information Based Feature Selection with Generalized Minimum-Redundancy and Maximum-Relevance Criteria

Short Term Electrical Load Forecasting Using Mutual Information Based Feature Selection with Generalized Minimum-Redundancy and Maximum-Relevance Criteria

A feature selection method based on the generalized minimum redundancy and maximum relevance (G-mRMR) is proposed to improve the accuracy of short-term load forecasting (STLF). First, mutual information is calculated to analyze the relations between the original features and the load sequence, as we...

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Bibliographic Details
Main Authors: Nantian Huang, Zhiqiang Hu, Guowei Cai, Dongfeng Yang
Format: Article
Language:English
Published: MDPI AG 2016-09-01
Series:Entropy
Subjects:
short term load forecasting
generalized minimum redundancy and maximum relevance
random forest
sequential forward selection
Online Access:http://www.mdpi.com/1099-4300/18/9/330

Internet

http://www.mdpi.com/1099-4300/18/9/330

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