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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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ankit Kumar Srivastava & Ajay Shekhar Pandey & Mohamad Abou Houran & Varun Kumar & Dinesh Kumar & Saurabh Mani Tripathi & Sivasankar Gangatharan & Rajvikram Madurai Elavarasan, 2023. "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," Energies, MDPI, vol. 16(2), pages 1-23, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:867-:d:1033102
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    References listed on IDEAS

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    1. Tso, Geoffrey K.F. & Yau, Kelvin K.W., 2007. "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks," Energy, Elsevier, vol. 32(9), pages 1761-1768.
    2. Kumar, Ujjwal & Jain, V.K., 2010. "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India," Energy, Elsevier, vol. 35(4), pages 1709-1716.
    3. Waqas Ahmad & Nasir Ayub & Tariq Ali & Muhammad Irfan & Muhammad Awais & Muhammad Shiraz & Adam Glowacz, 2020. "Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine," Energies, MDPI, vol. 13(11), pages 1-17, June.
    4. Ankit Kumar Srivastava & Ajay Shekhar Pandey & Rajvikram Madurai Elavarasan & Umashankar Subramaniam & Saad Mekhilef & Lucian Mihet-Popa, 2021. "A Novel Hybrid Feature Selection Method for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 14(24), pages 1-16, December.
    5. Stefan Ungureanu & Vasile Topa & Andrei Cristinel Cziker, 2021. "Analysis for Non-Residential Short-Term Load Forecasting Using Machine Learning and Statistical Methods with Financial Impact on the Power Market," Energies, MDPI, vol. 14(21), pages 1-26, October.
    6. Masoud Sobhani & Allison Campbell & Saurabh Sangamwar & Changlin Li & Tao Hong, 2019. "Combining Weather Stations for Electric Load Forecasting," Energies, MDPI, vol. 12(8), pages 1-11, April.
    7. Grzegorz Dudek, 2022. "A Comprehensive Study of Random Forest for Short-Term Load Forecasting," Energies, MDPI, vol. 15(20), pages 1-19, October.
    8. Matallanas, E. & Castillo-Cagigal, M. & Gutiérrez, A. & Monasterio-Huelin, F. & Caamaño-Martín, E. & Masa, D. & Jiménez-Leube, J., 2012. "Neural network controller for Active Demand-Side Management with PV energy in the residential sector," Applied Energy, Elsevier, vol. 91(1), pages 90-97.
    9. Wesseh, Presley K. & Zoumara, Babette, 2012. "Causal independence between energy consumption and economic growth in Liberia: Evidence from a non-parametric bootstrapped causality test," Energy Policy, Elsevier, vol. 50(C), pages 518-527.
    10. Shaoqian Pei & Hui Qin & Liqiang Yao & Yongqi Liu & Chao Wang & Jianzhong Zhou, 2020. "Multi-Step Ahead Short-Term Load Forecasting Using Hybrid Feature Selection and Improved Long Short-Term Memory Network," Energies, MDPI, vol. 13(16), pages 1-23, August.
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