The Taguchi-neural networks approach to forecast electricity consumption

M.F., Romlie and D., Purwanto and H., Agustiawan (2008) The Taguchi-neural networks approach to forecast electricity consumption. In: IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2008, 4 May 2008 through 7 May 2008, Niagara Falls, ON.

[thumbnail of paper.pdf] PDF
Restricted to Registered users only

Download (12kB)
Official URL:


Neural networks (NN) have been widely used for electricity forecasting, but some difficulties are still found. One of those difficulties is in choosing the optimal network parameter, which are strongly important to obtain accurate result. "Trial and error" commonly used to set the parameter is ineffective in terms of processing time and the accuracy. In this paper, Taguchi method is employed to optimize the accuracy of NN based prediction. This hybrid approach results in the optimal network parameters. Those are: 1 for the history length, 1 day for sampling time, and 8 nodes for hidden neurons. The method is used to predict electricity consumption in Universiti Teknologi PETRONAS (UTP), Malaysia. From the preliminary results it is found that the combined method seems to be a convincing approach. © 2008 IEEE.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Artificial intelligence; Electric power utilization; Forecasting; Neural networks; Taguchi methods; Technology; Combined methods; Electrical and computer engineering; Electricity consumption; Hidden neurons; History length; Hybrid approaches; Malaysia; Network parameters; PETRONAS; Processing Time; Sampling time; Taguchi; Taguchi's method; Trial and error; Electric load forecasting
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments / MOR / COE: Departments > Electrical & Electronic Engineering
Depositing User: Mohd Fakhizan Romlie
Date Deposited: 09 Mar 2010 02:01
Last Modified: 19 Jan 2017 08:26

Actions (login required)

View Item
View Item