TY  - CONF
Y1  - 2011///
SN  - 9781612848372
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-79957536756&doi=10.1109%2fICCRD.2011.5763864&partnerID=40&md5=c91705abba258f3d32746fd9687258b6
A1  - Ismail, M.J.
A1  - Ibrahim, R.
A1  - Ismail, I.
VL  - 4
EP  - 113
CY  - Shanghai
AV  - none
N2  - This paper discusses on the adaptive neural network model for predicting the energy consumption at a metering station. The function of the metering system is to calculate the energy consumption of the outgoing gas flow. To ensure the robustness of the developed model, it is suggested to make the model an adaptive model that will periodically update the weights. This will ensure the reliability of the model. A dynamic prediction model that can adapt itself to changes in the energy consumption pattern is desirable especially for short-term energy prediction. It is also important for an on-line running of the metering system. Two methods of weights update are proposed and tested, namely the accumulative training and sliding window training. The developed adaptive neural network model is then compared with the static neural network. Adaptive neural network for energy consumption has shown better result and recommended for implementation in the metering station. © 2011 IEEE.
N1  - cited By 7; Conference of 2011 3rd International Conference on Computer Research and Development, ICCRD 2011 ; Conference Date: 11 March 2011 Through 15 March 2011; Conference Code:84959
KW  - Adaptive models; Adaptive neural network models; Adaptive neural networks; Developed model; Dynamic prediction; Energy consumption; Energy prediction; Gas flows; Metering stations; Metering systems; Sliding Window; sliding window training method; Static neural networks; Training methods
KW  -  Electric load forecasting; Energy utilization; Forecasting; Mathematical models
KW  -  Neural networks
TI  - Adaptive neural network prediction model for energy consumption
SP  - 109
ID  - scholars2080
ER  -