Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Neuro-fuzzy systems have attracted growing interest of researchers in various scientific and engineering areas due to the increasing need of intelligent systems.  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 Por Exchange (VPX) and the actual energy demand. To evaluate,  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. © 2001 Elsevier Science B.V. All rights reserved.
['Ajith Abraham', 'Baikunth Nath']
I am writing a research paper on a idea A Neuro-Fuzzy Approach for Modelling Electricity Demand in Victoria
[{"from": "human", "value": "I am writing a research paper on a idea A Neuro-Fuzzy Approach for Modelling Electricity Demand in Victoria"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Neuro-fuzzy systems have attracted growing interest of researchers in various scientific and engineering areas due to the increasing need of intelligent systems.  evaluates the use of two popular soft computing techniques and conventional statistical approach based on Box\u2013Jenkins 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 Por Exchange (VPX) and the actual energy demand. To evaluate,  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. \u00a9 2001 Elsevier Science B.V. All rights reserved."}]