A Development of Very Short-Term Load Forecasting Based on Chaos Theory

Abstract

It is indispensable to accurately perform the 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 the conventional chaos theory. However, this approach is unable to give accurate forecasting results in case where the loads consecutively exceed than the historical maximum or lower than the minimum. Also, electric furnace loads with steep fluctuations have been another factor to degrade the forecast accuracy.
This paper presents an improved forecasting method based on Chaos theory. Especially, the potential of the Local Fuzzy Reconstruction Method, a variant of the localized reconstruction methods, is fully exploited to realize accurate forecast as much as possible. To resolve the forecast deterioration due to sudden change 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 given. The effectiveness of the proposed methods is verified by using real load data for one year.