Optimization FOU of Interval Type-2 Fuzzy Inference System Using Big Bang – Big Crunch Algorithm for Short Term Load Forecasting on National Holiday Case Study: South and Central Kalimantan-Indonesia

Abstract

The application interval type-2 fuzzy inference systems (IT2FIS) has been attention for a short-term load forecasting problems solution. This paper present optimization membership function of antecedent (X,Y) and consequent (Z) interval type-2 Fuzzy Logic System using Big Bang – Big Crunch Algorithm for application short term load forecasting on national holiday. This method has implemented on the historical peak load data during 14 national holiday case study in South and Central Kalimantan – Indonesia electrical power system on year 2008. The Big Bang–Big Crunch (BB–BC) algorithm will applied to optimization interval footprint of uncertainty (FOU) membership functions of interval type-2 fuzzy logic system. BB-BC Algorithm is a global optimization algorithm and has a low computational cost and a high convergence speed, so very efficient in this research. The test result showed main absolute percentage error (MAPE) less than interval type-2 fuzzy inference system (FIS) and type-1 fuzzy inference system (FIS). Finally, this paper defined main absolute percentage error (MAPE) 0.58045% for type-1 FIS, 0.53906% for interval type-2 FIS, and 0.52421% for optimization interval type-2 FIS-Big Bang Big Crunch Algorithm.