Electrical load time series data forecasting using interval type-2 fuzzy logic system

This paper describes about electrical load time series data forecasting using interval type-2 fuzzy logic system. This interval type-2 fuzzy logic is used as the method to forecast electrical load in East Java-Bali area from January until March 2007. The training data used in this research are electric load data from September 2005 until December 2006. The structure of Interval Type-2 Fuzzy Logic used in this research, has three fuzzy sets per input with uncertain mean Gaussian membership function. The number of input varies from two until five inputs in order to predict single output value. Steepest descent training algorithm is used to train interval type-2 fuzzy logic system. The training process was done by adjusting the used parameter so that this system can produce an output with minimum error. Experimental result showed that the interval type-2 fuzzy logic system could forecast East Java-Bali´s electrical load data well with the best root mean square error value of 0.082691. This was resulted from the experiment using 2 inputs, variance value of 0.2, and learning rate of 0.4.