SHORT-TERM PREDICTION OF PEAK LOAD USAGE AT PT.PLN (Persero) AREA SORONG USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS)
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Nurindah, Mega Dwi
Siregar, Riki Ruli A
Kuswardani, Dwina
Siregar, Riki Ruli A
Kuswardani, Dwina
Subject
Teknik Informatika
Datestamp
2022-09-22 08:05:37
Abstract :
PT. PLN (Persero) Sorong Area is currently in predicting still using historical load patterns. This can lead to material losses for both parties, both PT. PLN (Persero) Sorong Area and consumers. From these problems it is necessary to plan a prediction system using the method. The method of determining the peak load prediction value used in this study is the Adaptive Neuro-Fuzzy Inference System (ANFIS) method which is a combined method of Fuzzy Inference System with Artificial Neural Network (ANN). From the results of the testing, a comparison was made between the prediction results by ANFIS with the actual burden of electricity use in Sorong Kota. The results of calculations using alternative one obtained an average error reached 1.0183% with an accuracy of 98.9817% while for the second alternative obtained an average error reached 0.0124% with an accuracy of 99.9%. These results indicate that the prediction using ANFIS is appropriate and can be used to assist the generator parts staff at PT. PLN (Persero) Sorong Area.
PT. PLN (Persero) Sorong Area is currently in predicting still using historical load patterns. This can lead to material losses for both parties, both PT. PLN (Persero) Sorong Area and consumers. From these problems it is necessary to plan a prediction system using the method. The method of determining the peak load prediction value used in this study is the Adaptive Neuro-Fuzzy Inference System (ANFIS) method which is a combined method of Fuzzy Inference System with Artificial Neural Network (ANN). From the results of the testing, a comparison was made between the prediction results by ANFIS with the actual burden of electricity use in Sorong Kota. The results of calculations using alternative one obtained an average error reached 1.0183% with an accuracy of 98.9817% while for the second alternative obtained an average error reached 0.0124% with an accuracy of 99.9%. These results indicate that the prediction using ANFIS is appropriate and can be used to assist the generator parts staff at PT. PLN (Persero) Sorong Area.
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