Adaptive Machine Learning Techniques for Enhancing Smart Grid Data Integrity
Ensuring data integrity in smart power grids is crucial for their optimized operation and planning. However, the increasing penetration of renewable energy sources and the emergence of flexible loads like electric vehicles create significant uncertainties and complexities in data patterns. Tradition...
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| Formato: | Articulo |
| Lenguaje: | Inglés |
| Publicado: |
2024
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| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/176829 |
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| Sumario: | Ensuring data integrity in smart power grids is crucial for their optimized operation and planning. However, the increasing penetration of renewable energy sources and the emergence of flexible loads like electric vehicles create significant uncertainties and complexities in data patterns. Traditional centralized models struggle with data privacy concerns, communication overheads, and lack of model adaptiveness. This paper proposes adaptive machine-learning techniques for enhancing data integrity in smart grids. Local machine learning models are trained on distributed private datasets across different stations of the grid, and only the model parameters are communicated to a central server to create an aggregated global model, without exchanging any raw private data. The proposed approach harnesses edge resources efficiently through decentralized on-device training while providing enhanced accuracy and personalization over centralized models. Several experiments conducted on electricity consumption data validate the effectiveness of our approach in handling complex spatiotemporal changes and generating station-specific adaptive forecasts. By adopting a decentralized approach, our methodology seeks to enhance grid resilience by preserving data privacy, mitigating security risks, and optimizing the efficiency of smart microgrid operations. The proposed solution can enable optimized capacity planning and retail pricing for sustainable grids of the future. |
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