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Forecasting the electrical load becomes important, because it can estimate electricity consumption over a certain time range. Accuracy in electric load forecasting can improve safety and reliability in the operation of power systems such as load flow, maintenance of generating units and scheduling of generating units. In this study used case study system Sulselrabar, which is currently growing, but still not much to discuss about the condition of the current system and which will come. Several methods for predicting electrical loads have been widely used, ranging from conventional to smartbased methods. In this research will be proposed method of artificial intelligence for forecasting Short Term load on Sulselrabar system. The method used is based Fuzzy Logic and Cuckoo Search Algorithm. The combination of Fuzzy logic and Cuckoo Search methods is chosen because the combination of both optimizes optimum fuzzy logic membership, so the forecasting results have a very small error. From the results of the research can be concluded that the result of load forecasting using Fuzzy Logic method optimized using Cuckoo Search Algorithm (FL-CSA) is better than Fuzzy LONTAR KOMPUTER VOL. 8, NO. 3, DESEMBER 2017 p-ISSN 2088-1541 DOI : 10.24843/LKJITI.2017.v08.i03.p02 e-ISSN 2541-5832 155 Logic that is not optimized. The analysis results using input data 3 months before day H, to predict the load for one week on January 1 to 7 january 2014, and as a comparison used the predicted day H data. From the simulation results, the mean absolute percentage error (MAPE) is smaller using FLCSA, for the smallest MAPE on 1 January 2014 of 0.06785208%. While the highest MAPE on January 4, 2014 amounted to -0.44973%.

Intelligent Fuzzy Logic - Cuckoo Search Algorithm Method for Short-Term Electric Load Forecasting in 150 kV Sulselrabar System