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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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 2731-0809 |