Volume 7, Issue 4 (December 2011)                   IJEEE 2011, 7(4): 249-259 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Ghods L, Kalantar M. Different Methods of Long-Term Electric Load Demand Forecasting a Comprehensive Review. IJEEE 2011; 7 (4) :249-259
URL: http://ijeee.iust.ac.ir/article-1-301-en.html
Different Methods of Long-Term Electric Load Demand Forecasting a Comprehensive Review
L. Ghods , M. Kalantar
Abstract:   (12588 Views)
Long-term demand forecasting presents the first step in planning and developing future generation, transmission and distribution facilities. One of the primary tasks of an electric utility accurately predicts load demand requirements at all times, especially for long-term. Based on the outcome of such forecasts, utilities coordinate their resources to meet the forecasted demand using a least-cost plan. In general, resource planning is performed subject to numerous uncertainties. Expert opinion indicates that a major source of uncertainty in planning for future capacity resource needs and operation of existing generation resources is the forecasted load demand. This paper presents an overview of the past and current practice in long- term demand forecasting. It introduces methods, which consists of some traditional methods, neural networks, genetic algorithms, fuzzy rules, support vector machines, wavelet networks and expert systems.
Keywords: Long-Term, Demand Forecasting , Neural Networks , Genetic Algorithms , Fuzzy Rules
Full-Text [PDF 351 kb]   (16351 Downloads)    
Type of Study: Research Paper | Subject: Load Modeling Estimation&Forecasting
Received: 2010/07/04 | Revised: 2011/12/24 | Accepted: 2011/07/18
Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Creative Commons License
© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.