Peningkatan Akurasi Dalam Prakiraan Beban Listrik Jangka Pendek Menggunakan Data Temperatur

Abstract

Temperature changing has affected to the electrical load consumption. It has considered in the calculation of short-term load forecasting. In this study, ANFIS method was used to forecast short-term load using historical data of electrical load and temperature. From the testing process, the MAPE value has obtained during the procces for daily bunch from Monday to Sunday and Public Holiday is included, i.e. 8.567%; 5.013%; 5.341%; 4.214%; 7.741%; 6.635%; 6.875% and
0.022689%, respectively. The addicting of temperature data buffering has proven that forecasting accuracy is increased.
Keywords : Load forecasting, Short-term load forecasting, Neural Network, Fuzzy-Inference, ANFIS, MATLAB