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A Comparative Study of Forecasting Electricity Consumption Using Machine Learning Models

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  • Madeline Hui Li Lee

    (Department of Actuarial Science and Applied Statistics, Faculty of Business & Management, UCSI University, Jalan Menara Gading, Cheras, Kuala Lumpur 56000, Malaysia)

  • Yee Chee Ser

    (Department of Actuarial Science and Applied Statistics, Faculty of Business & Management, UCSI University, Jalan Menara Gading, Cheras, Kuala Lumpur 56000, Malaysia)

  • Ganeshsree Selvachandran

    (Department of Actuarial Science and Applied Statistics, Faculty of Business & Management, UCSI University, Jalan Menara Gading, Cheras, Kuala Lumpur 56000, Malaysia)

  • Pham Huy Thong

    (VNU Information Technology Institute, Vietnam National University, Hanoi 03000, Vietnam)

  • Le Cuong

    (VNU Information Technology Institute, Vietnam National University, Hanoi 03000, Vietnam)

  • Le Hoang Son

    (VNU Information Technology Institute, Vietnam National University, Hanoi 03000, Vietnam)

  • Nguyen Trung Tuan

    (School of Information Technology and Digital Economics, National Economic University, Hanoi 100000, Vietnam)

  • Vassilis C. Gerogiannis

    (Department of Digital Systems, Faculty of Technology, University of Thessaly, Geopolis, 41500 Larissa, Greece)

Abstract

Production of electricity from the burning of fossil fuels has caused an increase in the emission of greenhouse gases. In the long run, greenhouse gases cause harm to the environment. To reduce these gases, it is important to accurately forecast electricity production, supply and consumption. Forecasting of electricity consumption is, in particular, useful for minimizing problems of overproduction and oversupply of electricity. This research study focuses on forecasting electricity consumption based on time series data using different artificial intelligence and metaheuristic methods. The aim of the study is to determine which model among the artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), least squares support vector machines (LSSVMs) and fuzzy time series (FTS) produces the highest level of accuracy in forecasting electricity consumption. The variables considered in this research include the monthly electricity consumption over the years for different countries. The monthly electricity consumption data for seven countries, namely, Norway, Switzerland, Malaysia, Egypt, Algeria, Bulgaria and Kenya, for 10 years were used in this research. The performance of all of the models was evaluated and compared using error metrics such as the root mean squared error (RMSE), average forecasting error (AFE) and performance parameter (PP). The differences in the results obtained via the different methods are analyzed and discussed, and it is shown that the different models performed better for different countries in different forecasting periods. Overall, it was found that the FTS model performed the best for most of the countries studied compared to the other three models. The research results can allow electricity management companies to have better strategic planning when deciding on the optimal levels of electricity production and supply, with the overall aim of preventing surpluses or shortages in the electricity supply.

Suggested Citation

  • Madeline Hui Li Lee & Yee Chee Ser & Ganeshsree Selvachandran & Pham Huy Thong & Le Cuong & Le Hoang Son & Nguyen Trung Tuan & Vassilis C. Gerogiannis, 2022. "A Comparative Study of Forecasting Electricity Consumption Using Machine Learning Models," Mathematics, MDPI, vol. 10(8), pages 1-23, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:8:p:1329-:d:795708
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    References listed on IDEAS

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    Cited by:

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    3. Siyu Zhang & Liusan Wu & Ming Cheng & Dongqing Zhang, 2022. "Prediction of Whole Social Electricity Consumption in Jiangsu Province Based on Metabolic FGM (1, 1) Model," Mathematics, MDPI, vol. 10(11), pages 1-14, May.
    4. Marwa Salah EIDin Fahmy & Farhan Ahmed & Farah Durani & Štefan Bojnec & Mona Mohamed Ghareeb, 2023. "Predicting Electricity Consumption in the Kingdom of Saudi Arabia," Energies, MDPI, vol. 16(1), pages 1-20, January.

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