Abstract: A system, method and computer program for forecasting energy price is provided that includes an adaptive hybrid forecasting engine. The adaptive hybrid forecasting engine is operable to generate an energy price forecast based on both a prediction utility and a correction utility. The prediction utility may implement a linear modeling algorithm for predicting energy price based on historical data. The linear modeling algorithm may be a multiplicative seasonal ARIMA (Autoregressive Integrated Moving Average) model, for example, which includes both a regular ARIMA and seasonal ARIMA model. The correction utility may implement an adaptive dynamic correction algorithm that is operable to adapt the energy price forecast based on current or near-current conditions. The adaptive dynamic correction algorithm may be a LL (lazy learning) algorithm.