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Computer Science > Artificial Intelligence

arXiv:cs/0405010 (cs)
[Submitted on 5 May 2004]

Title:A Neuro-Fuzzy Approach for Modelling Electricity Demand in Victoria

Authors:Ajith Abraham, Baikunth Nath
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Abstract: Neuro-fuzzy systems have attracted growing interest of researchers in various scientific and engineering areas due to the increasing need of intelligent systems. This paper evaluates the use of two popular soft computing techniques and conventional statistical approach based on Box--Jenkins autoregressive integrated moving average (ARIMA) model to predict electricity demand in the State of Victoria, Australia. The soft computing methods considered are an evolving fuzzy neural network (EFuNN) and an artificial neural network (ANN) trained using scaled conjugate gradient algorithm (CGA) and backpropagation (BP) algorithm. The forecast accuracy is compared with the forecasts used by Victorian Power Exchange (VPX) and the actual energy demand. To evaluate, we considered load demand patterns for 10 consecutive months taken every 30 min for training the different prediction models. Test results show that the neuro-fuzzy system performed better than neural networks, ARIMA model and the VPX forecasts.
Subjects: Artificial Intelligence (cs.AI)
ACM classes: I.2.0
Cite as: arXiv:cs/0405010 [cs.AI]
  (or arXiv:cs/0405010v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.cs/0405010
Journal reference: Applied Soft Computing Journal, Elsevier Science, Volume 1&2, pp. 127-138, 2001

Submission history

From: Ajith Abraham [view email]
[v1] Wed, 5 May 2004 00:27:53 UTC (495 KB)
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