Multiple local minimum points often exist on the surface of forecasting error function of the time series models. 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 natural evolutionary process, the evolutionary programming (EP) algorithm offers the capability of converging towards the global extremum of a complex error surface. The EP based load forecasting algorithm is developed to identify the autoregression moving average (ARMA) model for one week ahead hourly load demand forecasts. Numerical tests indicate the proposed EP approach provides a method to simultaneously estimate the appropriate order and parameter values of the ARMA model for diverse types of load data. Comparisons of forecasting errors are made to the traditional identification techniques used by SAS statistical commercial package.
|出版狀態||Published - 1995 十二月 1|
|事件||Proceedings of the 1995 International Conference on Energy Management and Power Delivery, EMPD'95. Part 1 (of 2) - Singapore, Singapore|
持續時間: 1995 十一月 21 → 1995 十一月 23
|Other||Proceedings of the 1995 International Conference on Energy Management and Power Delivery, EMPD'95. Part 1 (of 2)|
|期間||95-11-21 → 95-11-23|
All Science Journal Classification (ASJC) codes