Machine learning models accurately forecast photovoltaic and AC power output for a grid-connected solar plant

Machine learning models, specifically Random Forest, Decision Trees, and Deep Learning, achieved near-perfect accuracy in forecasting photovoltaic and AC power output. These models utilized historical sensor data including solar irradiance, ambient temperature, wind speed, and cell temperature to predict power generation over multiple time horizons. The accurate forecasts can aid grid operators in managing the variability of solar power and optimizing the integration of this renewable energy source.