Long Term Electrical Load Forecasting via a Neurofuzzy Model
Publish place: 14th annual International CSI Computer Conference
Publish Year: 1388
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
CSICC14_055
تاریخ نمایه سازی: 24 خرداد 1388
Abstract:
Long-term forecasting of load demand is necessary for the correct operation of electric utilities. There is an on-going attention toward putting new approaches to the task. Recently, Neurofuzzy modeling has played a successful role in various applications over nonlinear time series prediction. This paper presents a neurofuzzy model for long-term load forecasting. This model is identified through Locally Linear Model Tree (LoLiMoT) learning algorithm. The model is compared to a multilayer perceptron and hierarchical hybrid neural model (HHNM). The models are trained and assessed on load data extracted from a North- American electric utility.
Authors
M Nosrati Maralloo
Department of Computer Engineering , Science and Research Branch, Islamic Azad University, Tehran,Iran
A.R Koushki
Department of Computer Engineering , Science and Research Branch, Islamic Azad University, Tehran,Iran
C Lucas
Control and Intelligent Processing Center of Excellence, Electrical and Computer Engineering Department, University of Tehran,Tehran,Iran
A Kalhor
Control and Intelligent Processing Center of Excellence, Electrical and Computer Engineering Department, University of Tehran,Tehran,Iran