Multivariate inputs for electrical load forecasting on hybrid neuro-fuzzy and fuzzy C-Means forecaster

Multivariate inputs play important role in system with many dependent variables. By using some different inputs as input in neuro-fuzzy networks, complex nonlinear model can be modeled and also be forecasted with better results. This paper describes a neuro-fuzzy approach with additional fuzzy C-means clustering method before the input entering the networks. Afterwards, the network can be used to efficiently forecast electrical load competition data using the Takagi-Sugeno (TS) type multi-input single-output (MISO) neuro-fuzzy network. The training algorithm is efficient in the sense that it can bring the performance index of the network, such as the sum squared error (SSE), down to the desired error goal much faster than the simple Levenberg-Marquardt algorithm (LMA).