Juhász, Kristóf Péter and Vokony, István and Hartmann, Bálint and Béres, Ferenc and Könyves, Vera (2025) Machine Learning-Based Transmission Loss Prediction and Mitigation Strategies for the Hungarian Network. In: 2025 IEEE Kiel PowerTech. IEEE, Piscataway (NJ), p. 11180430. ISBN 9798331543976 10.1109/PowerTech59965.2025.11180430

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Abstract

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.

Item Type:	Book Section
Subjects:	Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions:	Artificial Intelligence Laboratory
SWORD Depositor:	MTMT Injector
Depositing User:	MTMT Injector
Date Deposited:	28 Jan 2026 20:56
Last Modified:	28 Jan 2026 20:56
URI:	https://eprints.sztaki.hu/id/eprint/11102

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