Title :
Long-term load forecasting in electricity market
Author :
Daneshi, Hossein ; Shahidehpour, Mohammad ; Choobbari, Azim Lotfjou
Author_Institution :
Electr. Power & Power Electron. Center, Illinois Inst. of Technol., Chicago, IL
Abstract :
Long-term load forecasting has a vital role in generation, transmission and distribution network planning. Traditional studies for long-term load forecasting were based on regression method, which could not provide a true representation of power system behavior in a volatile electricity market. The purpose of this paper is to introduce two approaches based regression method and artificial neural network (ANN) for long-term load forecast. We apply fuzzy sets to ANN for modeling long-term uncertainties and compare the enhanced forecasting results with those of traditional methods. The ISO New England market data are used to illustrate the efficiency of each technique.
Keywords :
fuzzy set theory; neural nets; power engineering computing; power markets; power system economics; power system planning; regression analysis; ISO New England market data; artificial neural network; distribution network planning; electricity market; fuzzy sets; generation planning; long-term load forecasting; regression method; transmission planning; Artificial neural networks; Economic forecasting; Electricity supply industry; Fuzzy sets; ISO; Load forecasting; Power system modeling; Power system planning; Predictive models; Uncertainty; artificial neural network; fuzzy system; long-term load forecasting; power system planning; regression method;
Conference_Titel :
Electro/Information Technology, 2008. EIT 2008. IEEE International Conference on
Conference_Location :
Ames, IA
Print_ISBN :
978-1-4244-2029-2
Electronic_ISBN :
978-1-4244-2030-8
DOI :
10.1109/EIT.2008.4554335