Towards an Explainable Artificial Intelligence approach for smart grid systems
Abstract As global energy demands escalate, effective management of electrical grids and reducing carbon emissions have become critical objectives. This paper proposes a novel system which employs Explainable Artificial Intelligence (XAI) to enhance the operational efficiency of smart grids by predi...
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| Format: | Article |
| Language: | English |
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Springer
2025-04-01
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| Series: | Discover Artificial Intelligence |
| Online Access: | https://doi.org/10.1007/s44163-025-00261-5 |
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| _version_ | 1850139110472155136 |
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| author | Mahmoud Alfayan Hani Hagras |
| author_facet | Mahmoud Alfayan Hani Hagras |
| author_sort | Mahmoud Alfayan |
| collection | DOAJ |
| description | Abstract As global energy demands escalate, effective management of electrical grids and reducing carbon emissions have become critical objectives. This paper proposes a novel system which employs Explainable Artificial Intelligence (XAI) to enhance the operational efficiency of smart grids by predicting energy consumption and optimising resource allocation accordingly. The integration of XAI addresses the complexities of grid management by providing transparency into AI-driven predictions, thus fostering user trust and facilitating informed decision-making. The main contributions of this work include evaluating the status of smart energy grid management and presenting a pathway for integrating XAI to enhance these practices. Using a real-world dataset with variables related to operations, environment, and time, our system employs a type-2 fuzzy logic XAI based system to generate clear, interpretable predictions of energy demand. The transition from a Type-1 to a Type-2 fuzzy logic system resulted in enhanced prediction performance, as evidenced by a reduction in the root mean square error (RMSE) from 8.7734 to 5.9422, resulting in 32.2% enhancement in RMSE. The proposed system demonstrated comparable performance compared to conventional black-box models, including neural network. It also shows stakeholders how to interpret these predictions across a wide range of consumption scenarios, which emphasizes the importance of predictions in making the best use of resources. Ultimately, this paper presents a first step for the employment of XAI within smart grid management. |
| format | Article |
| id | doaj-art-a544c50f968f48edbf60ded9f8b54f45 |
| institution | OA Journals |
| issn | 2731-0809 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Artificial Intelligence |
| spelling | doaj-art-a544c50f968f48edbf60ded9f8b54f452025-08-20T02:30:24ZengSpringerDiscover Artificial Intelligence2731-08092025-04-015112010.1007/s44163-025-00261-5Towards an Explainable Artificial Intelligence approach for smart grid systemsMahmoud Alfayan0Hani Hagras1School of Computer Science and Electronic Engineering, University of EssexSchool of Computer Science and Electronic Engineering, University of EssexAbstract As global energy demands escalate, effective management of electrical grids and reducing carbon emissions have become critical objectives. This paper proposes a novel system which employs Explainable Artificial Intelligence (XAI) to enhance the operational efficiency of smart grids by predicting energy consumption and optimising resource allocation accordingly. The integration of XAI addresses the complexities of grid management by providing transparency into AI-driven predictions, thus fostering user trust and facilitating informed decision-making. The main contributions of this work include evaluating the status of smart energy grid management and presenting a pathway for integrating XAI to enhance these practices. Using a real-world dataset with variables related to operations, environment, and time, our system employs a type-2 fuzzy logic XAI based system to generate clear, interpretable predictions of energy demand. The transition from a Type-1 to a Type-2 fuzzy logic system resulted in enhanced prediction performance, as evidenced by a reduction in the root mean square error (RMSE) from 8.7734 to 5.9422, resulting in 32.2% enhancement in RMSE. The proposed system demonstrated comparable performance compared to conventional black-box models, including neural network. It also shows stakeholders how to interpret these predictions across a wide range of consumption scenarios, which emphasizes the importance of predictions in making the best use of resources. Ultimately, this paper presents a first step for the employment of XAI within smart grid management.https://doi.org/10.1007/s44163-025-00261-5 |
| spellingShingle | Mahmoud Alfayan Hani Hagras Towards an Explainable Artificial Intelligence approach for smart grid systems Discover Artificial Intelligence |
| title | Towards an Explainable Artificial Intelligence approach for smart grid systems |
| title_full | Towards an Explainable Artificial Intelligence approach for smart grid systems |
| title_fullStr | Towards an Explainable Artificial Intelligence approach for smart grid systems |
| title_full_unstemmed | Towards an Explainable Artificial Intelligence approach for smart grid systems |
| title_short | Towards an Explainable Artificial Intelligence approach for smart grid systems |
| title_sort | towards an explainable artificial intelligence approach for smart grid systems |
| url | https://doi.org/10.1007/s44163-025-00261-5 |
| work_keys_str_mv | AT mahmoudalfayan towardsanexplainableartificialintelligenceapproachforsmartgridsystems AT hanihagras towardsanexplainableartificialintelligenceapproachforsmartgridsystems |