TY  - CONF
Y1  - 2008///
SN  - 08407789
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-51849155634&doi=10.1109%2fCCECE.2008.4564882&partnerID=40&md5=19f88e00301bcef95ee666f9c085958b
A1  - Purwanto, D.
A1  - Agustiawan, H.
A1  - Romlie, M.F.
EP  - 1944
CY  - Niagara Falls, ON
AV  - none
N2  - 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.
N1  - cited By 2; Conference of IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2008 ; Conference Date: 4 May 2008 Through 7 May 2008; Conference Code:73468
KW  - Artificial intelligence; Electric power utilization; Forecasting; Neural networks; Taguchi methods; Technology
KW  -  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
KW  -  Electric load forecasting
TI  - The Taguchi-neural networks approach to forecast electricity consumption
SP  - 1941
ID  - scholars439
ER  -