Identification of ARMAX model for short term load forecasting: an evolutionary programming approach

This paper proposes a new evolutionary programming (EP) approach to identify the autoregressive moving average with exogenous variable (ARMAX) model for one day to one week ahead hourly load demand forecasts. Typically, the surface of forecasting error function possesses multiple local minimum points. Solutions of the traditional gradient search based identification technique therefore may stall at the local optimal points which lead to an inadequate model. By simulating a natural evolutionary process, the EP algorithm offers the capability of converging towards the global extremum of a complex error surface. The developed EP based load forecasting algorithm is verified by using different types of data for the Taiwan Power (Taipower) system and substation load as well as temperature values. Numerical results indicate the proposed EP approach provides a method to simultaneously estimate the appropriate order and parameter values of the ARMAX model for diverse types of load data. Comparisons of forecasting errors are made to the traditional identification techniques.