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Evaluation of Machine Learning Models for Smart Grid Parameters: Performance Analysis of ARIMA and Bi-LSTM

Author

Listed:
  • Yuanhua Chen

    (College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
    School of Automobile Engineering, Guilin University of Aerospace Technology, Guilin 541004, China)

  • Muhammad Shoaib Bhutta

    (School of Automobile Engineering, Guilin University of Aerospace Technology, Guilin 541004, China)

  • Muhammad Abubakar

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China)

  • Dingtian Xiao

    (School of Automobile Engineering, Guilin University of Aerospace Technology, Guilin 541004, China)

  • Fahad M. Almasoudi

    (Department of Electrical Engineering, Faculty of Engineering, University of Tabuk, Tabuk 47913, Saudi Arabia)

  • Hamad Naeem

    (Department of Computer Science, King Faisal University, Hofuf 31982, Saudi Arabia)

  • Muhammad Faheem

    (Department of Computing Technology and Innovations, University of Vaasa, 65200 Vaasa, Finland)

Abstract

The integration of renewable energy resources into smart grids has become increasingly important to address the challenges of managing and forecasting energy production in the fourth energy revolution. To this end, artificial intelligence (AI) has emerged as a powerful tool for improving energy production control and management. This study investigates the application of machine learning techniques, specifically ARIMA (auto-regressive integrated moving average) and Bi-LSTM (bidirectional long short-term memory) models, for predicting solar power production for the next year. Using one year of real-time solar power production data, this study trains and tests these models on performance measures such as mean absolute error (MAE) and root mean squared error (RMSE). The results demonstrate that the Bi-LSTM (bidirectional long short-term memory) model outperforms the ARIMA (auto-regressive integrated moving average) model in terms of accuracy and is able to successfully identify intricate patterns and long-term relationships in the real-time-series data. The findings suggest that machine learning techniques can optimize the integration of renewable energy resources into smart grids, leading to more efficient and sustainable power systems.

Suggested Citation

  • Yuanhua Chen & Muhammad Shoaib Bhutta & Muhammad Abubakar & Dingtian Xiao & Fahad M. Almasoudi & Hamad Naeem & Muhammad Faheem, 2023. "Evaluation of Machine Learning Models for Smart Grid Parameters: Performance Analysis of ARIMA and Bi-LSTM," Sustainability, MDPI, vol. 15(11), pages 1-25, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8555-:d:1155118
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    Cited by:

    1. Namrye Son & Yoonjeong Shin, 2023. "Short- and Medium-Term Electricity Consumption Forecasting Using Prophet and GRU," Sustainability, MDPI, vol. 15(22), pages 1-16, November.

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