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A Novel Hybrid Feature Selection Method for Day-Ahead Electricity Price Forecasting

Author

Listed:
  • Ankit Kumar Srivastava

    (Department of Electrical Engineering, Dr. Rammanohar Lohia Avdh University, Ayodhya 224001, India)

  • Ajay Shekhar Pandey

    (Department of Electrical Engineering, Kamla Nehru Institute of Technology, Sultanpur 228118, India)

  • Rajvikram Madurai Elavarasan

    (Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai 625015, India)

  • Umashankar Subramaniam

    (Renewable Energy Laboratory, Department of Communications and Networks, College of Engineering, Prince Sultan University, Riyadh 11586, Saudi Arabia)

  • Saad Mekhilef

    (School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC 3122, Australia)

  • Lucian Mihet-Popa

    (Faculty of Electrical Engineering, Ostfold University College, 1757 Halden, Norway)

Abstract

The paper proposes a novel hybrid feature selection (FS) method for day-ahead electricity price forecasting. The work presents a novel hybrid FS algorithm for obtaining optimal feature set to gain optimal forecast accuracy. The performance of the proposed forecaster is compared with forecasters based on classification tree and regression tree. A hybrid FS method based on the elitist genetic algorithm (GA) and a tree-based method is applied for FS. Making use of selected features, aperformance test of the forecaster was carried out to establish the usefulness of the proposed approach. By way of analyzing and forecasts for day-ahead electricity prices in the Australian electricity markets, the proposed approach is evaluated and it has been established that, with the selected feature, the proposed forecaster consistently outperforms the forecaster with a larger feature set. The proposed method is simulated in MATLAB and WEKA software.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8455-:d:702668
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    References listed on IDEAS

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    Cited by:

    1. Jun Dong & Xihao Dou & Aruhan Bao & Yaoyu Zhang & Dongran Liu, 2022. "Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China," Sustainability, MDPI, vol. 14(13), pages 1-24, June.
    2. Yangrui Zhang & Peng Tao & Xiangming Wu & Chenguang Yang & Guang Han & Hui Zhou & Yinlong Hu, 2022. "Hourly Electricity Price Prediction for Electricity Market with High Proportion of Wind and Solar Power," Energies, MDPI, vol. 15(4), pages 1-13, February.
    3. 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.

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