Akademik Veri Yönetim Sistemi
ENERGIES, cilt.19, sa.2, ss.1-15, 2026 (SCI-Expanded, Scopus)
Accurate forecasting of meteorological parameters is essential for the reliable operation and
performance optimization of photovoltaic (PV) power plants. Among these parameters,
ambient temperature and global horizontal irradiance (GHI) have the most direct impact
on PV output. This study investigates the integration of machine-learning-based (ML)
weather forecasts into PV energy modeling and quantifies how forecast accuracy propagates
into PV generation estimation errors. Three commonly used ML algorithms—Artificial
Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest (RF)—were
developed and compared. Antalya (Turkey), representing a Mediterranean climate zone,
was selected as the case study location. High-resolution meteorological data from 2018–2023
were used to train and evaluate the forecasting models for prediction horizons from
1 to 10 days. Model performance was assessed using root mean square error (RMSE)
and the coefficient of determination (R2). The results indicate that RF provides the highest
accuracy for temperature prediction, while ANN demonstrates superior performance for
GHI forecasting. The generated forecasts were incorporated into a PV power output
simulation using the PVLib library. The analysis reveals that inaccuracies in GHI forecasts
have the largest impact on PV energy estimation, whereas temperature forecast errors
contribute significantly less. Overall, the study demonstrates the practical benefits of
integrating ML-based meteorological forecasting with PV performance modeling and
provides guidance on selecting suitable forecasting techniques for renewable energy system
planning and optimization.