Volume 6, Issue 3 (September 2010)                   IJEEE 2010, 6(3): 175-182 | Back to browse issues page

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Ghods L, Kalantar M. Long-Term Peak Demand Forecasting by Using Radial Basis Function Neural Networks. IJEEE. 2010; 6 (3) :175-182
URL: http://ijeee.iust.ac.ir/article-1-319-en.html
Abstract:   (11115 Views)
Prediction of peak loads in Iran up to year 2011 is discussed using the Radial Basis Function Networks (RBFNs). In this study, total system load forecast reflecting the current and future trends is carried out for global grid of Iran. Predictions were done for target years 2007 to 2011 respectively. Unlike short-term load forecasting, long-term load forecasting is mainly affected by economy factors rather than weather conditions. This study focuses on economical data that seem to have influence on long-term electric load demand. The data used are: actual yearly, incremental growth rate from previous year, and blend (actual and incremental growth rate from previous years). As the results, the maximum demands for 2007 through 2011 are predicted and is shown to be elevated from 37138 MW to 45749 MW for Iran Global Grid. The annual average rate of load growth seen per five years until 2011 is about 5.35%
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Type of Study: Research Paper | Subject: Evolutionary Computation
Received: 2010/09/15 | Accepted: 2013/12/30 | Published: 2013/12/30