A Day-Ahead Short-Term Load Forecasting Using M5P Machine Learning Algorithm along with Elitist Genetic Algorithm (EGA) and Random Forest-Based Hybrid Feature Selection

A Day-Ahead Short-Term Load Forecasting Using M5P Machine Learning Algorithm along with Elitist Genetic Algorithm (EGA) and Random Forest-Based Hybrid Feature Selection

A hybrid feature selection (HFS) algorithm to obtain the optimal feature set to attain optimal forecast accuracy for short-term load forecasting (STLF) problems is proposed in this paper. The HFS employs an elitist genetic algorithm (EGA) and random forest method, which is embedded in the load forec...

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Bibliographic Details
Main Authors: Ankit Kumar Srivastava, Ajay Shekhar Pandey, Mohamad Abou Houran, Varun Kumar, Dinesh Kumar, Saurabh Mani Tripathi, Sivasankar Gangatharan, Rajvikram Madurai Elavarasan
Format: Article
Language:English
Published: MDPI AG 2023-01-01
Series:Energies
Subjects:
confidence interval
elitist genetic algorithm
feature selection
short-term load forecasting
M5P forecaster
machine learning
Online Access:https://www.mdpi.com/1996-1073/16/2/867

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