Abstract-This paper presents two hybrid neural networks derived from fuzzy neural networks (FNN): wavelet fuzzy neural network (WFNN) using the fuzzified wavelet features as the inputs to FNN and fuzzy neural network (FNCI) employing the Choquet integral as the outputs of FNN. The learning through FNCI is simplified by the use of q-measure and the speed of convergence of the parameters is increased by reinforced learning. The underlying fuzzy models of these hybrid networks are a modified form of fuzzy rules of Takagi-Sugeno model. The number of fuzzy rules is found from a fuzzy curve corresponding to each input-output by counting the total number of peaks and troughs in the curve. The models can forecast hourly load with a lead time of 1 h as they deal with short-term load forecasting. The results of the two hybrid networks using Indian utility data are compared with ANFIS and other conventional methods. The performance of the proposed WFNN is found superior to all the other compared methods.Index Terms-Fuzzy systems, neural networks, short-term load forecasting, wavelet transforms and Choquet integral.

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This paper presents a Wavelet Fuzzy Neural Network (WFNN) that takes the fuzzified wavelet features as inputs to Fuzzy Neural Network. This network is constructed from the fuzzy rules which are modified form of the fuzzy rules of Takagi-Sugeno fuzzy model. The number of fuzzy rules is found from the fuzzy curve approach. As the output of the model is the forecasted demand, we need a fuzzy curve corresponding to each input-output. The model can forecast hourly load with a lead time of one hour as this work is concerned with short-term electric load forecasting. Electric Load demand data and weather variables are procured from Northern Region Load Dispatch Centre and Mausam Bhawan, Delhi (India) respectively. The results of the network are compared with ANFIS and other conventional methods. The performance of the proposed Wavelet Fuzzy Neural Network is found to be superior to all others compared.