Forecasting of the Capacity Factor of a Photovoltaic System Using Artificial Intelligence and Machine Learning Modeling
Year: 2025
Type: conference-paper
Abstract: Accurate forecasting of the Capacity Factor (CF) of Photovoltaic (PV) systems is vital for optimizing energy output, grid stability, and economic performance. This study applies Artificial Neural Network (ANN) modeling in the MATLAB environment, using seven years (2018–2024) of data from the Renewables.ninja open database, for Athens, Greece. Inputs include meteorological parameters, irradiance patterns, and system performance. The models are evaluated for prediction accuracy, computational efficiency, and adaptability. Results show that ANN modeling significantly improves CF forecasts, offering critical insights for energy planners and stakeholders, and supporting data-driven strategies in sustainable energy management and grid planning.
Institution: University of West Attica
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: University of West Attica
OpenAlex ID: W4414244478