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Machine Learning-Based Approach to Predict Energy Consumption of Renewable and Nonrenewable Power Sources

Author

Listed:
  • Prince Waqas Khan

    (Department of Computer Engineering, Jeju National University, Jeju-si 63243, Korea)

  • Yung-Cheol Byun

    (Department of Computer Engineering, Jeju National University, Jeju-si 63243, Korea)

  • Sang-Joon Lee

    (Department of Computer Engineering, Jeju National University, Jeju-si 63243, Korea)

  • Dong-Ho Kang

    (Power Technology Development Team, HODI, Mapo Ssangyong Geum Building 3f, Mapo-gu, Seoul 04178, Korea)

  • Jin-Young Kang

    (Jeju Regional Headquarter, Korea Power Exchange, 81, Ora-NamRo, Jeju 63144, Korea)

  • Hae-Su Park

    (Jeju Regional Headquarter, Korea Power Exchange, 81, Ora-NamRo, Jeju 63144, Korea)

Abstract

In today’s world, renewable energy sources are increasingly integrated with nonrenewable energy sources into electric grids and pose new challenges because of their intermittent and variable nature. Energy prediction using soft-computing techniques plays a vital role in addressing these challenges. As electricity consumption is closely linked to other energy sources such as natural gas and oil, forecasting electricity consumption is essential for making national energy policies. In this paper, we utilize various data mining techniques, including preprocessing historical load data and the load time series’s characteristics. We analyzed the power consumption trends from renewable energy sources and nonrenewable energy sources and combined them. A novel machine learning-based hybrid approach, combining multilayer perceptron (MLP), support vector regression (SVR), and CatBoost, is proposed in this paper for power forecasting. A thorough comparison is made, taking into account the results obtained using other prediction methods.

Suggested Citation

  • Prince Waqas Khan & Yung-Cheol Byun & Sang-Joon Lee & Dong-Ho Kang & Jin-Young Kang & Hae-Su Park, 2020. "Machine Learning-Based Approach to Predict Energy Consumption of Renewable and Nonrenewable Power Sources," Energies, MDPI, vol. 13(18), pages 1-16, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4870-:d:415090
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    References listed on IDEAS

    as
    1. Jungwon Yu & June Ho Park & Sungshin Kim, 2018. "A New Input Selection Algorithm Using the Group Method of Data Handling and Bootstrap Method for Support Vector Regression Based Hourly Load Forecasting," Energies, MDPI, vol. 11(11), pages 1-20, October.
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    5. Prince Waqas Khan & Yung-Cheol Byun & Sang-Joon Lee & Namje Park, 2020. "Machine Learning Based Hybrid System for Imputation and Efficient Energy Demand Forecasting," Energies, MDPI, vol. 13(11), pages 1-23, May.
    6. Gabriel Mendonça de Paiva & Sergio Pires Pimentel & Bernardo Pinheiro Alvarenga & Enes Gonçalves Marra & Marco Mussetta & Sonia Leva, 2020. "Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks," Energies, MDPI, vol. 13(11), pages 1-28, June.
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    Cited by:

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    4. Can Ding & Yiyuan Zhou & Qingchang Ding & Kaiming Li, 2022. "Integrated Carbon-Capture-Based Low-Carbon Economic Dispatch of Power Systems Based on EEMD-LSTM-SVR Wind Power Forecasting," Energies, MDPI, vol. 15(5), pages 1-27, February.
    5. Yukta Mehta & Rui Xu & Benjamin Lim & Jane Wu & Jerry Gao, 2023. "A Review for Green Energy Machine Learning and AI Services," Energies, MDPI, vol. 16(15), pages 1-30, July.
    6. Hussan, Umair & Wang, Huaizhi & Peng, Jianchun & Jiang, Hui & Rasheed, Hamna, 2026. "Transformer-based renewable energy forecasting: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PC).
    7. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    8. Prince Waqas Khan & Yongjun Kim & Yung-Cheol Byun & Sang-Joon Lee, 2021. "Influencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction," Energies, MDPI, vol. 14(21), pages 1-22, November.
    9. Rong Qu & Ruibing Kou & Tianyi Zhang, 2024. "The Impact of Weather Variability on Renewable Energy Consumption: Insights from Explainable Machine Learning Models," Sustainability, MDPI, vol. 17(1), pages 1-25, December.
    10. Gomez, William & Wang, Fu-Kwun & Sheu, Shey-Huei, 2025. "Short-term smart grid energy forecasting using a hybrid deep learning method on univariate and multivariate data sets," Energy, Elsevier, vol. 335(C).

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