Advancements and future outlook of Artificial Intelligence in energy and climate change modeling
This paper explores the employment of artificial intelligence and machine learning to decipher strategic responses to incidences of climate change and to inform the management of energy systems. Given the increasing global dependence on sustainable and efficient energy solutions and the rise of arti...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Advances in Applied Energy |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666792425000058 |
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| author | Mobolaji Shobanke Mehul Bhatt Ekundayo Shittu |
| author_facet | Mobolaji Shobanke Mehul Bhatt Ekundayo Shittu |
| author_sort | Mobolaji Shobanke |
| collection | DOAJ |
| description | This paper explores the employment of artificial intelligence and machine learning to decipher strategic responses to incidences of climate change and to inform the management of energy systems. Given the increasing global dependence on sustainable and efficient energy solutions and the rise of artificial intelligence and machine learning, it has become imperative to evaluate existing routines in energy and climate change modeling to identify areas for further application. The process of conducting a systematic review of the contemporary literature highlights significant advances in optimization and predictive analytics within energy and climate change modeling systems driven by artificial intelligence and machine learning. This paper contributes to cutting-edge research on energy innovation, i.e., through the examination of the applications of artificial intelligence and machine learning in energy modeling and climate change assessments. The article bridges the gaps between research, development, and implementation with significant insights into the broader applications of artificial intelligence and machine learning in the analysis of future energy transitions and climate change mitigation and adaptation. |
| format | Article |
| id | doaj-art-f3d7143bf72c4a9aa0a88e09dd379e54 |
| institution | Kabale University |
| issn | 2666-7924 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Advances in Applied Energy |
| spelling | doaj-art-f3d7143bf72c4a9aa0a88e09dd379e542025-02-12T05:32:58ZengElsevierAdvances in Applied Energy2666-79242025-03-0117100211Advancements and future outlook of Artificial Intelligence in energy and climate change modelingMobolaji Shobanke0Mehul Bhatt1Ekundayo Shittu2Department of Engineering Management and Systems Engineering, The George Washington University, Washington, DC, 20052, USADepartment of Engineering Management and Systems Engineering, The George Washington University, Washington, DC, 20052, USACorresponding author.; Department of Engineering Management and Systems Engineering, The George Washington University, Washington, DC, 20052, USAThis paper explores the employment of artificial intelligence and machine learning to decipher strategic responses to incidences of climate change and to inform the management of energy systems. Given the increasing global dependence on sustainable and efficient energy solutions and the rise of artificial intelligence and machine learning, it has become imperative to evaluate existing routines in energy and climate change modeling to identify areas for further application. The process of conducting a systematic review of the contemporary literature highlights significant advances in optimization and predictive analytics within energy and climate change modeling systems driven by artificial intelligence and machine learning. This paper contributes to cutting-edge research on energy innovation, i.e., through the examination of the applications of artificial intelligence and machine learning in energy modeling and climate change assessments. The article bridges the gaps between research, development, and implementation with significant insights into the broader applications of artificial intelligence and machine learning in the analysis of future energy transitions and climate change mitigation and adaptation.http://www.sciencedirect.com/science/article/pii/S2666792425000058Artificial Intelligence (AI)Machine Learning (ML)Energy modelsClimate change modelsPredictive analyticsMeta-analysis |
| spellingShingle | Mobolaji Shobanke Mehul Bhatt Ekundayo Shittu Advancements and future outlook of Artificial Intelligence in energy and climate change modeling Advances in Applied Energy Artificial Intelligence (AI) Machine Learning (ML) Energy models Climate change models Predictive analytics Meta-analysis |
| title | Advancements and future outlook of Artificial Intelligence in energy and climate change modeling |
| title_full | Advancements and future outlook of Artificial Intelligence in energy and climate change modeling |
| title_fullStr | Advancements and future outlook of Artificial Intelligence in energy and climate change modeling |
| title_full_unstemmed | Advancements and future outlook of Artificial Intelligence in energy and climate change modeling |
| title_short | Advancements and future outlook of Artificial Intelligence in energy and climate change modeling |
| title_sort | advancements and future outlook of artificial intelligence in energy and climate change modeling |
| topic | Artificial Intelligence (AI) Machine Learning (ML) Energy models Climate change models Predictive analytics Meta-analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2666792425000058 |
| work_keys_str_mv | AT mobolajishobanke advancementsandfutureoutlookofartificialintelligenceinenergyandclimatechangemodeling AT mehulbhatt advancementsandfutureoutlookofartificialintelligenceinenergyandclimatechangemodeling AT ekundayoshittu advancementsandfutureoutlookofartificialintelligenceinenergyandclimatechangemodeling |