A Review for Green Energy Machine Learning and AI Services

Yukta Mehta, Rui Xu, Benjamin Lim, Jane Wu and Jerry Gao ()
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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

Energies, 2023, vol. 16, issue 15, 1-30

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.

Keywords: green AI services; load forecasting; price forecasting; energy usage; load profiling; smart-grid; machine learning (ML) technologies; deep learning (DL) technologies (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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