Fuzzy modeling for short term load forecasting using the orthogonal least squares method

A fuzzy modeling method is developed in this paper for short term load forecasting. According to this method, identification of the premise part and consequent part is separately accomplished via the orthogonal least squares (OLS) technique. Particularly, the OLS is first employed to partition the input space and determine the number of fuzzy rules and the premise parameters. In the sequel, a second orthogonal estimator determines the input terms which should be included in the consequent part of each fuzzy rule and calculate its parameters. Input selection is automatically performed, given an input candidate set of arbitrary size, formulated by an expert. A satisfactory prediction performance is attained as shown in the test results, showing the effectiveness of the suggested method.