This chapter presents a comprehensive analysis of predictive models for forecasting solar generation in microgrids, with a focus on optimizing energy management for systems supporting electric vehicle charging stations. Various methodologies are examined, including simple exponential smoothing (SESM and its variant SESM‑2), seasonal autoregressive integrated moving average (SARIMA), regression‐based approaches (linear, ridge, and lasso), the ensemble random forest method, and advanced deep-learning techniques such as long short-term memory (LSTM) networks. Forecast performance is evaluated through error metrics–MAE, RMSE, and R2–and benchmarking against average base values. While simpler models like SESM‑2 deliver fast implementation and low computational overhead, their limited ability to capture seasonality and abrupt weather changes reduces accuracy under dynamic conditions. In contrast, the SARIMA and regression models capture periodic trends but may struggle with transient fluctuations. The LSTM model excels by handling long-term dependencies and complex nonlinearities, achieving the highest precision. These insights offer practical guidelines for selecting forecasting tools tailored to specific operational demands, ensuring enhanced reliability and resilience in microgrid operations.