Development of very-short-term load forecasting based on chaos theory

Abstract

It is indispensable to accurately perform short-term load forecasting of 10 minutes ahead in order to avoid undesirable disturbances in power system operations. The authors have so far developed such a forecasting method based on conventional chaos theory. However, this approach cannot give accurate forecasting results when the loads consecutively exceed the historical maximum or are less than the minimum. Electric furnace loads with steep fluctuations are another factor degrading the forecast accuracy. This paper presents an improved forecasting method based on chaos theory. In particular, the potential of the Local Fuzzy Reconstruction Method, a variant of the localized reconstruction methods, is fully exploited to realize accurate forecasting as much as possible. To resolve the forecast deterioration due to suddenly changing loads such as by electric furnaces, they are separated from the rest and smoothing operations are carried out afterwards. The separated loads are forecasted independently from the remaining components. Several error correction methods are incorporated to enhance the proposed forecasting method. Furthermore, a consistent measure of obtaining the optimal combination of parameters to be used in the forecasting method is presented. The effectiveness of the proposed methods is verified by using real load data for 1 year. © 2004 Wiley Periodicals, Inc.