Studies on Highly accurate Short-term Electric Power Load Forecasting with Fuzzy Data Mining

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In this project, a new hybrid intelligent system has been proposed for short-term load forecasting in power systems. The proposed method is based on the regression tree of data mining, Simplified Fuzzy Inference and Tabu Search of Meta-heuristics. The regression tree works to extract rules from data base though the decision tree so that if-then rules are obtained. Simplified Fuzzy Inference (SFI) is a good nonlinear approximation technique for nonlinear systems that is equivalent to the multi-layered perceptron (MLP) of artificial neural network. The use of Tabu Search allows SFI to construct the globally optimal rules in terms of the number of fuzzy membership functions and their location. As a result, the proposed model is superior to MLP in terms of the prediction error. At the same time, SFI is applied to the regression tree to improve the boundary conditions of the splitting conditions. The fuzzy rules contributed to the classification of data on load forecasting.Also, the use of TS is easier to determine the forecasting model from a standpoint of minimizing the maximum errors of load forecasting model through the learning process due to the advantage without any constraints. Therefore, power system operators have flexibility to give priority to the maximum or the average squared errors.In addition, the developed model contributed to the reduction of the reserves of generation so that it plays an important role as the decision making system of selling and buying the electricity and make power system operation and control more effective.