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

Ankit Kumar Srivastava, Ajay Shekhar Pandey, Mohamad Abou Houran, Varun Kumar, Dinesh Kumar, Saurabh Mani Tripathi, Sivasankar Gangatharan and Rajvikram Madurai Elavarasan ()
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Ankit Kumar Srivastava: Electrical Engineering Department, Dr. Rammanohar Lohia Avadh University, Ayodhya 224001, India
Ajay Shekhar Pandey: Department of Electrical Engineering, Kamla Nehru Institute of Technology, Sultanpur 228118, India
Mohamad Abou Houran: School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Varun Kumar: Department of Electrical Engineering, Kamla Nehru Institute of Technology, Sultanpur 228118, India
Dinesh Kumar: Electrical Engineering Department, Dr. Rammanohar Lohia Avadh University, Ayodhya 224001, India
Saurabh Mani Tripathi: Department of Electrical Engineering, Kamla Nehru Institute of Technology, Sultanpur 228118, India
Sivasankar Gangatharan: Electrical & Electronics Engineering Department, Thiagarajar College of Engineering, Madurai 625015, India
Rajvikram Madurai Elavarasan: School of Information Technology and Electrical Engineering, The University of Queensland, St. Lucia, QLD 4072, Australia

Energies, 2023, vol. 16, issue 2, 1-23

Abstract: 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 forecasting algorithm for online feature selection (FS). Using selected features, the performance of the forecaster was tested to signify the utility of the proposed methodology. For this, a day-ahead STLF using the M5P forecaster (a comprehensive forecasting approach using the regression tree concept) was implemented with FS and without FS (WoFS). The performance of the proposed forecaster (with FS and WoFS) was compared with the forecasters based on J48 and Bagging. The simulation was carried out in MATLAB and WEKA software. Through analyzing short-term load forecasts for the Australian electricity markets, evaluation of the proposed approach indicates that the input feature selected by the HFS approach consistently outperforms forecasters with larger feature sets.

Keywords: confidence interval; elitist genetic algorithm; feature selection; short-term load forecasting; M5P forecaster; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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