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

Abstract

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.

Day-ahead load forecast using random forest and expert input selection

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Correlation Coefficient Method for Support Vector Machine Input Samples

“…In recently years, support vector machine method is the research hotspot of load forecasting at all times. Many schemes for different support vector machine structure and training methods are presented [1,2]. But the relative systemic approach for support vector machine input variables selecting can't be proposed in these schemes, which usually select input variables according to the experience of designers.…”

Forecasting of telecommunications time-series via an Orthogonal Least Squares-based fuzzy model

“…In the present work the modeling method proposed in [8] is employed: The fuzzy region described by (3) is regarded as a fuzzy hyper-cell centered at } ..., ,…”