AI-based forecasting for optimised solar energy management and smart grid efficiency
This paper considers two pertinent research inquiries: ‘Can an AI-based predictive framework be utilised for the optimisation of solar energy management?’ and ‘What are the ways in which the AI-based predictive framework can be integrated within the Smart Grid infrastructure to improve grid reliabil...
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| Format: | Article |
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Informa UK Limited
2024
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| Online Access: | https://hdl.handle.net/1721.1/153988 |
| _version_ | 1811077779734659072 |
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| author | Bouquet, Pierre Jackson, Ilya Nick, Mostafa Kaboli, Amin |
| author_facet | Bouquet, Pierre Jackson, Ilya Nick, Mostafa Kaboli, Amin |
| author_sort | Bouquet, Pierre |
| collection | MIT |
| description | This paper considers two pertinent research inquiries: ‘Can an AI-based predictive framework be utilised for the optimisation of solar energy management?’ and ‘What are the ways in which the AI-based predictive framework can be integrated within the Smart Grid infrastructure to improve grid reliability and efficiency?’ The study deploys a Deep Learning model based on Long Short-Term Memory techniques, leading to refined accuracy in solar electricity generation forecasts. Such an AI-supported methodology aids power grid operators in comprehensive planning, thereby ensuring a robust electricity supply. The effectiveness of this framework is tested using performance metrics such as MAE, RMSE, nMAE, nRMSE, and R2. A persistent model is utilised as a reference for comparison. Despite a slight decrease in predictive precision with the expansion of the forecast horizon, the proposed AI-based framework consistently surpasses the persistent model, particularly for horizons beyond two hours. Therefore, this research underscores the potential of AI-based prediction in fostering efficient solar energy management and enhancing Smart Grid reliability and efficiency. |
| first_indexed | 2024-09-23T10:48:15Z |
| format | Article |
| id | mit-1721.1/153988 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T10:48:15Z |
| publishDate | 2024 |
| publisher | Informa UK Limited |
| record_format | dspace |
| spelling | mit-1721.1/1539882024-04-19T05:43:00Z AI-based forecasting for optimised solar energy management and smart grid efficiency Bouquet, Pierre Jackson, Ilya Nick, Mostafa Kaboli, Amin Industrial and Manufacturing Engineering Management Science and Operations Research Strategy and Management This paper considers two pertinent research inquiries: ‘Can an AI-based predictive framework be utilised for the optimisation of solar energy management?’ and ‘What are the ways in which the AI-based predictive framework can be integrated within the Smart Grid infrastructure to improve grid reliability and efficiency?’ The study deploys a Deep Learning model based on Long Short-Term Memory techniques, leading to refined accuracy in solar electricity generation forecasts. Such an AI-supported methodology aids power grid operators in comprehensive planning, thereby ensuring a robust electricity supply. The effectiveness of this framework is tested using performance metrics such as MAE, RMSE, nMAE, nRMSE, and R2. A persistent model is utilised as a reference for comparison. Despite a slight decrease in predictive precision with the expansion of the forecast horizon, the proposed AI-based framework consistently surpasses the persistent model, particularly for horizons beyond two hours. Therefore, this research underscores the potential of AI-based prediction in fostering efficient solar energy management and enhancing Smart Grid reliability and efficiency. 2024-04-01T19:06:44Z 2024-04-01T19:06:44Z 2023-10-16 Article http://purl.org/eprint/type/JournalArticle 0020-7543 1366-588X https://hdl.handle.net/1721.1/153988 Pierre Bouquet, Ilya Jackson, Mostafa Nick & Amin Kaboli (2023) AI-based forecasting for optimised solar energy management and smart grid efficiency, International Journal of Production Research. 10.1080/00207543.2023.2269565 International Journal of Production Research Creative Commons Attribution-NonCommercial-NoDerivs License https://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Informa UK Limited Author |
| spellingShingle | Industrial and Manufacturing Engineering Management Science and Operations Research Strategy and Management Bouquet, Pierre Jackson, Ilya Nick, Mostafa Kaboli, Amin AI-based forecasting for optimised solar energy management and smart grid efficiency |
| title | AI-based forecasting for optimised solar energy management and smart grid efficiency |
| title_full | AI-based forecasting for optimised solar energy management and smart grid efficiency |
| title_fullStr | AI-based forecasting for optimised solar energy management and smart grid efficiency |
| title_full_unstemmed | AI-based forecasting for optimised solar energy management and smart grid efficiency |
| title_short | AI-based forecasting for optimised solar energy management and smart grid efficiency |
| title_sort | ai based forecasting for optimised solar energy management and smart grid efficiency |
| topic | Industrial and Manufacturing Engineering Management Science and Operations Research Strategy and Management |
| url | https://hdl.handle.net/1721.1/153988 |
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