 Various predictive models have been developed for load forecasting based on various techniques like multiple regression, exponential smoothing, iterative reweighted least-squares, adaptive load forecasting, stochastic time series, fuzzy logic, neural networks and knowledge based expert systems.Amongst these, the most popular STLF were stochastic time series models like Autoregressive (AR) model, Autoregressive moving average model (ARMA), Autoregressive integrated moving average (ARIMA) model and other models using fuzzy logic and Neural Networks.DLF provides data aggregation and forecasting capabilities that is configured to address today’s requirements and adapt to address future requirements and should have the capability to produce repeatable and accurate forecasts.