Machine Learning-Based Transmission Loss Prediction and Mitigation Strategies for the Hungarian Network

Network loss is a fundamental metric that reflects the efficiency of a power system's ability to transmit energy across its network. This work investigates the application of machine learning algorithms for predicting transmission network losses in the Hungarian transmission grid. The research focuses on enhancing forecasting precision by utilizing supervised learning methods trained on historical data, incorporating key variables such as weather conditions, and energy flows. Additionally, the study evaluates loss reduction strategies and explores innovative approaches, including the integration of photovoltaic systems and energy storage solutions. Results demonstrate the potential of machine learning to surpass traditional prediction techniques, offering a scalable framework for improving the operational efficiency and sustainability of transmission networks.