A constrained orthogonal least-squares method for generating TSK fuzzy models: Application to short-term load forecasting

In this paper, an orthogonal least-squares (OLS) based modeling method is d eveloped, named the constrained OLS (C-OLS), for generating simple and effi cient TSK fuzzy models. The method is a two-stage model building technique, where both premise and consequent identification are simultaneously perfor med. The fuzzy system is considered as a linear regression model by decompo sing the TSK; model into a collection of generic rules. The C-OLS algorithm is employed at stage-1 to identify the structure of the model. Given a mod el building data set, the algorithm selects a subset of most significant re gressors which should be included in the model. Based on the similarity mea sure, a classification tool is developed, which organizes the selected term s into groups with similar premise parts, forming TSK rules. Additionally, input variable selection for the consequent part is performed. The resultin g model is reduced in complexity by discarding the unnecessary terms, and i s optimized at stage-2 using a richer training data set. This method is use d to generate fuzzy models for a real-world problem, the load forecasting o f the Greek power system. Extensive simulation results are given and discus sed, demonstrating the effectiveness of the suggested method. (C) 2001 Else vier Science B.V. All rights reserved.