High-resolution photovoltaic power forecasting using machine learning models under seasonal and stress conditions.

Accurately predicting how much power a photovoltaic (PV) system will produce is essential for keeping modern smart grids stable. This research framework tackles the difficulty of making high‑resolution PV forecasts by testing several machine‑learning designs under different seasons and harsh weather conditions. First, we clean and prepare detailed weather data and past power‑generation records to pull out important time‑based and location‑based features. Then we build advanced models—Long Short‑Term Memory (LSTM) networks, Extreme Gradient Boosting (XGBoost), and hybrid convolutional‑recurrent structures—to learn the nonlinear relationships and sudden changes in solar irradiance. We pay special attention to how the models behave during stress events such as fast cloud cover shifts, extreme temperature spikes,

and seasonal transitions. Their performance is measured with standard statistics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the R‑squared coefficient. The implementation offers a clear way to compare each model’s robustness, computational speed, and prediction accuracy. This helps grid operators plan schedules, dispatch power, and manage reserve capacity more effectively.

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