Loading...
Please wait, while we are loading the content...
Similar Documents
Long-Term Load Forecasting on the Java-Madura-Bali Electricity System Using Artificial Neural Network Method
| Content Provider | Semantic Scholar |
|---|---|
| Author | Kuncoro, Arief Heru Zuhal Dalimi, Rinaldy |
| Copyright Year | 2007 |
| Abstract | LONG-TERM LOAD FORECASTING ON THE JAVA-MADURA-BALI ELECTRICITY SYSTEM USING ARTIFICIAL NEURAL NETWORK METHOD. A long-term forecasting of electric power peak load on the Java-Bali electricity system using Artificial Neural Network (ANN) method has been researched. Result has been compared with the forecasting from National Electricity General Plan (NEGP), with the study period of 2007 2025. ANN is a part of Artificial Intelligence (AI) that promises new generation of information-processing systems that demonstrate the ability to learn, recall, and generalize from training patterns or data. The ANN model used in this research is back propagation (BP). The research uses MATLAB version R2006b, and the steps of ANN methodology applied are as follow: assembling the training set data (TSD), creating the network object, training the network using historical data, simulating the network response to new inputs, and finally resulting in the output of forecasting. TSD consists of 2 data types, i.e.: input data (consist of: Gross Regional Domestic Product (GRDP), population, number of households, total electric energy demand, electricity consumption on households, electricity consumption on commercial sector, electricity consumption on public sector, electricity consumption on industry sector, electric energy on the Java-Bali system, & electrification ratio), and output target data (consist of: historical peak load data). The result of peak load forecasting using ANN method is reasonable and considered good enough, since the electricity utility will accept error until 10% in long term forecasting. Based on ANN approach, the Java-Bali system's peak load of the years 2007, 2010, 2015, 2020 and 2025 are predicted to be 16270 MW, 19740 MW, 28150 MW, 40270 MW and 57030 MW respectively, meanwhile according to NEGP are 17008 MW, 21152 MW, 30575 MW, 43018 MW and 59107 MW respectively. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://digilib.batan.go.id/e-prosiding/Icanse/article/B3.3-Arief_Long-rev.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
