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Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand

Author

Listed:
  • Nahid Sultana

    (Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia)

  • S. M. Zakir Hossain

    (Department of Chemical Engineering, College of Engineering, University of Bahrain, Zallaq 32038, Bahrain)

  • Salma Hamad Almuhaini

    (Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia)

  • Dilek Düştegör

    (Faculty of Science and Engineering, University of Groningen, 9747 AG Groningen, The Netherlands)

Abstract

This article focuses on developing both statistical and machine learning approaches for forecasting hourly electricity demand in Ontario. The novelties of this study include (i) identifying essential factors that have a significant effect on electricity consumption, (ii) the execution of a Bayesian optimization algorithm (BOA) to optimize the model hyperparameters, (iii) hybridizing the BOA with the seasonal autoregressive integrated moving average with exogenous inputs (SARIMAX) and nonlinear autoregressive networks with exogenous input (NARX) for modeling separately short-term electricity demand for the first time, (iv) comparing the model’s performance using several performance indicators and computing efficiency, and (v) validation of the model performance using unseen data. Six features (viz., snow depth, cloud cover, precipitation, temperature, irradiance toa, and irradiance surface) were found to be significant. The Mean Absolute Percentage Error (MAPE) of five consecutive weekdays for all seasons in the hybrid BOA-NARX is obtained at about 3%, while a remarkable variation is observed in the hybrid BOA-SARIMAX. BOA-NARX provides an overall steady Relative Error (RE) in all seasons (1~6.56%), while BOA-SARIMAX provides unstable results (Fall: 0.73~2.98%; Summer: 8.41~14.44%). The coefficient of determination (R 2 ) values for both models are >0.96. Overall results indicate that both models perform well; however, the hybrid BOA-NARX reveals a stable ability to handle the day-ahead electricity load forecasts.

Suggested Citation

  • Nahid Sultana & S. M. Zakir Hossain & Salma Hamad Almuhaini & Dilek Düştegör, 2022. "Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand," Energies, MDPI, vol. 15(9), pages 1-26, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3425-:d:810446
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    References listed on IDEAS

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    Cited by:

    1. Filipe Rodrigues & Carlos Cardeira & João M. F. Calado & Rui Melicio, 2023. "Short-Term Load Forecasting of Electricity Demand for the Residential Sector Based on Modelling Techniques: A Systematic Review," Energies, MDPI, vol. 16(10), pages 1-26, May.
    2. Mustafa Saglam & Xiaojing Lv & Catalina Spataru & Omer Ali Karaman, 2024. "Instantaneous Electricity Peak Load Forecasting Using Optimization and Machine Learning," Energies, MDPI, vol. 17(4), pages 1-22, February.
    3. Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2023. "Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms," Energies, MDPI, vol. 16(11), pages 1-23, June.
    4. Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2022. "Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island," Energies, MDPI, vol. 15(16), pages 1-22, August.

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