A Review for Green Energy Machine Learning and AI Services
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/15/5718 |
| _version_ | 1797586849245429760 |
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| author | Yukta Mehta Rui Xu Benjamin Lim Jane Wu Jerry Gao |
| author_facet | Yukta Mehta Rui Xu Benjamin Lim Jane Wu Jerry Gao |
| author_sort | Yukta Mehta |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T00:28:59Z |
| format | Article |
| id | doaj.art-feee725c91df4cbfbb310fda47ac4c78 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T00:28:59Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-feee725c91df4cbfbb310fda47ac4c782023-11-18T22:51:56ZengMDPI AGEnergies1996-10732023-07-011615571810.3390/en16155718A Review for Green Energy Machine Learning and AI ServicesYukta Mehta0Rui Xu1Benjamin Lim2Jane Wu3Jerry Gao4Department of Applied Data Science, San Jose State University, San Jose, CA 95192, USABRI, San Francisco, CA 94104, USABRI, San Francisco, CA 94104, USABRI, San Francisco, CA 94104, USADepartment of Applied Data Science, San Jose State University, San Jose, CA 95192, USAThere 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.https://www.mdpi.com/1996-1073/16/15/5718green AI servicesload forecastingprice forecastingenergy usageload profilingsmart-grid |
| spellingShingle | Yukta Mehta Rui Xu Benjamin Lim Jane Wu Jerry Gao A Review for Green Energy Machine Learning and AI Services Energies green AI services load forecasting price forecasting energy usage load profiling smart-grid |
| title | A Review for Green Energy Machine Learning and AI Services |
| title_full | A Review for Green Energy Machine Learning and AI Services |
| title_fullStr | A Review for Green Energy Machine Learning and AI Services |
| title_full_unstemmed | A Review for Green Energy Machine Learning and AI Services |
| title_short | A Review for Green Energy Machine Learning and AI Services |
| title_sort | review for green energy machine learning and ai services |
| topic | green AI services load forecasting price forecasting energy usage load profiling smart-grid |
| url | https://www.mdpi.com/1996-1073/16/15/5718 |
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