DocumentCode :
2706232
Title :
Methods for long-term electric load demand forecasting; a comprehensive investigation
Author :
Ghods, Ladan ; Kalantar, Mohsen
Author_Institution :
Dept. of Electr. Eng., Iran Univ. of Sci. & Technol. (IUST), Tehran
fYear :
2008
fDate :
21-24 April 2008
Firstpage :
1
Lastpage :
4
Abstract :
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 longterm demand forecasting. It introduces methods, which consists of some traditional methods, neural networks, genetic algorithms, fuzzy rules and wavelet networks.
Keywords :
electricity supply industry; load forecasting; power system planning; electric utility; fuzzy rules; genetic algorithms; least-cost plan; long-term electric load demand forecasting; neural networks; resource planning; wavelet networks; Artificial intelligence; Automation; Demand forecasting; Economic forecasting; Genetic algorithms; Power generation; Power system modeling; Power system planning; Power system reliability; Uncertainty; Fuzzy Rules; Genetic Algorithms; Long-term; Neural networks; demand forecasting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Technology, 2008. ICIT 2008. IEEE International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-1705-6
Electronic_ISBN :
978-1-4244-1706-3
Type :
conf
DOI :
10.1109/ICIT.2008.4608469
Filename :
4608469
Link To Document :
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