Efficient Distributed PV Power Prediction via Blockchain-Driven Personalized Federated Learning

Accurate prediction of distributed photovoltaic (PV) power generation is crucial for efficient grid integration and energy management. Traditional centralized models often face challenges related to data privacy, heterogeneity, and transparency. This paper proposes an innovative framework that integrates blockchain technology with personalized federated learning (PFL) to address these issues in PV power prediction. In our approach, each participating node trains a local model on its dataset, preserving data privacy. Model updates are then securely aggregated using blockchain, ensuring transparency and immutability. This decentralized methodology accommodates the unique characteristics of each node's data, leading to improved predictin accuracy. Experimental results demonstrate that the proposed framework outperforms traditional centralized and non-personalized federated learning models. The integration of blockchain enhances data security and fosters trust among stakeholders. These findings underscore the potential of combining blockchain-driven personalized federated learning to advance distributed PV power prediction systems.