ENERGY FORECASTING USING MODEL PARAMETER ESTIMATION

摘要

A seasonal autoregressive moving average (SARIMA) model that represents seasonal variation and a linear regression method to reflect peak load variation due to temperature are used to forecast energy needs. In one embodiment, a Kalman filter is used for model parameter estimation. The use of the Kalman filter provides optimal parameter estimation even under noisy data conditions. It also handles iterative computations of the SARIMA model efficiently. The method provides a forecast every fifteen minutes in one embodiment for several buildings. A user specifies a building ID, number of history data and the date for which the forecast is to be done. A model underlying the data is identified, parameters are estimated for the model, and a forecast is provided for future consumption.