Peramalan Beban Listrik Jangka Pendek Dengan Menggunakan
Metode Regresi Linier
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Bagaskara, Bima
Pahiyanti, Novi Gusti
Pahiyanti, Novi Gusti
Subject
Teknik Elektro
Datestamp
2019-12-05 12:47:14
Abstract :
Short-term electricity load forecasting is a very important factor in the planning and operation of electric power systems. The purpose of electricity load forecasting is that demand and supply of electricity can be balanced. The pattern of consumption of electrical loads is different each time interval at the Harapan Indah Gas Insulated Switchgear (GIS) substation. The selection of the GIS Harapan Indah substation is based on the fact that it is a new substation operating since September 2017. It makes the electrical load variance problem not homogeneous. There are many methods used to produce accurate and precise electrical load forecasting. With the hope that the current load is channeled right and according to consumer needs. In this study discusses short-term electricity load forecasting with Linear Regression and Energy Coefficients methods wherein the two methods are compared to find the percentage value of a small error and the level of accuracy is very good. The calculation in this study uses historical electric load data at PT PLN (Persero) Greater Jakarta Distribution Unit in March 2019 on GIS Harapan Indah. The results showed that the comparison of average errors from Monday to Sunday value of 7.92% with an accuracy value of 92.08% for Linear Regression and 10.46% with an accuracy value of 89.54% for the Energy Coefficient
Short-term electricity load forecasting is a very important factor in the planning and operation of electric power systems. The purpose of electricity load forecasting is that demand and supply of electricity can be balanced. The pattern of consumption of electrical loads is different each time interval at the Harapan Indah Gas Insulated Switchgear (GIS) substation. The selection of the GIS Harapan Indah substation is based on the fact that it is a new substation operating since September 2017. It makes the electrical load variance problem not homogeneous. There are many methods used to produce accurate and precise electrical load forecasting. With the hope that the current load is channeled right and according to consumer needs. In this study discusses short-term electricity load forecasting with Linear Regression and Energy Coefficients methods wherein the two methods are compared to find the percentage value of a small error and the level of accuracy is very good. The calculation in this study uses historical electric load data at PT PLN (Persero) Greater Jakarta Distribution Unit in March 2019 on GIS Harapan Indah. The results showed that the comparison of average errors from Monday to Sunday value of 7.92% with an accuracy value of 92.08% for Linear Regression and 10.46% with an accuracy value of 89.54% for the Energy Coefficient
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