Forecasting renewable energy generation is crucial for improving the efficiency and reliability of power systems that integrate wind, solar, and other renewable sources. These energy sources are inherently variable, depending on changing weather patterns, which makes accurate forecasting a complex task. The ability to predict renewable energy production with high accuracy can help grid operators optimize energy storage, reduce the reliance on fossil fuels, and ensure grid stability. Forecasting methods vary significantly, ranging from physical models that rely on weather data, to statistical models and advanced machine learning techniques. Each method has its own strengths and limitations, and the choice of approach often depends on the specific requirements, such as time horizon, data availability, and computational resources.
