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A Review for Green Energy Machine Learning and AI Services

Author

Listed:
  • Yukta Mehta

    (Department of Applied Data Science, San Jose State University, San Jose, CA 95192, USA)

  • Rui Xu

    (BRI, San Francisco, CA 94104, USA)

  • Benjamin Lim

    (BRI, San Francisco, CA 94104, USA)

  • Jane Wu

    (BRI, San Francisco, CA 94104, USA)

  • Jerry Gao

    (Department of Applied Data Science, San Jose State University, San Jose, CA 95192, USA
    BRI, San Francisco, CA 94104, USA
    Department of Computer Engineering, San Jose State University, San Jose, CA 95192, USA)

Abstract

There is a growing demand for Green AI (Artificial Intelligence) technologies in the market and society, as it emerges as a promising technology. Green AI technologies are used to create sustainable solutions and reduce the environmental impact of AI. This paper focuses on describing the services of Green AI and the challenges associated with it at the community level. This article also highlights the accuracy levels of machine learning algorithms for various time periods. The process of choosing the appropriate input parameters for weather, locations, and complexity is outlined in this paper to examine the ML algorithms. For correcting the algorithm performance parameters, metrics like RMSE (root mean square error), MSE (mean square error), MAE (mean absolute error), and MPE (mean percentage error) are considered. Considering the performance and results of this review, the LSTM (long short-term memory) performed well in most cases. This paper concludes that highly advanced techniques have dramatically improved forecasting accuracy. Finally, some guidelines are added for further studies, needs, and challenges. However, there is still a need for more solutions to the challenges, mainly in the area of electricity storage.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5718-:d:1207294
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    References listed on IDEAS

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    Cited by:

    1. Jian Zhu & Zhiyuan Zhao & Xiaoran Zheng & Zhao An & Qingwu Guo & Zhikai Li & Jianling Sun & Yuanjun Guo, 2023. "Time-Series Power Forecasting for Wind and Solar Energy Based on the SL-Transformer," Energies, MDPI, vol. 16(22), pages 1-15, November.
    2. Behnam Zakeri, 2024. "Digitalization for Resilient and Sustainable Energy Transitions," Energies, MDPI, vol. 17(21), pages 1-4, October.

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