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Electric load in Nusa Dua Bali has increased from 2013-2017 by an average of 11.83%. The increase in electric load requires the electrical energy service provider to be able to adjust the electricity demand and be able to increase its reliability, The effort that can be done is to predict the electric load. Electric load forecasting can be done by various methods, ANFISo (Adaptiveo Neuroo Fuzzyo Inferenceo Systemo) is one method that is often used in forecasting electrical loads. ANFIS is able to explain the reasoning process and do data learning. The data used are the electric load, temperature, humidity and time, the data was chosen because changes in temperature and humidity affect people's habitual patterns in using air conditioners (electric load patterns). The electric load pattern is trained 100 times using ANFIS with the type of membership function is trimf, and [3 3 3 3] is the number of membership function. The indicator to determining the accuracy of the electrical load forecasting pattern results with the real electric load pattern used the MAPE (Mean Absolute Percentage Error) value, which the MAPE standard value that good is less than 10%. The test results from this study produced a MAPE value of 6.98%.
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