A Hybrid Blockchain and Machine Learning Framework for Loss-Aware Optimization in Distributed Photovoltaic Systems

Abstract

This study proposes a hybrid framework integrating machine learning and blockchain technologies for loss-aware optimization in distributed photovoltaic (PV) systems. The framework combines data-driven loss prediction, decentralized data validation, and optimization-based energy management to improve operational efficiency under variable generation and demand conditions. An LSTM-based model is employed to estimate system losses, while a blockchain layer with smart contracts provides transparent and tamper-resistant execution of operational decisions. The predicted losses are incorporated into the optimization process to determine more efficient energy-routing and grid-interaction strategies. Simulation-based evaluation demonstrates that the proposed framework outperforms the baseline and machine-learning-only configurations. In the investigated case, the energy-loss rate decreases from 14.2% in the baseline scenario to 6.3% with the proposed framework, corresponding to a 55.6% relative reduction. The results also indicate improvements in operational cost and overall system efficiency. These findings demonstrate the potential of combining predictive intelligence with decentralized validation for efficient and secure management of distributed PV systems.