Adaptive Neural Network Prediction for Energy Consumption

Ismail, M. J. and Ibrahim, R. and Ismail, Idris (2011) Adaptive Neural Network Prediction for Energy Consumption. In: 2011 International Conference on System Engineering and Modeling, 11 - 13 March 2011, Shanghai, China. (Submitted)

[thumbnail of ICSEM_maryamjamela__rev2_March11.pdf] PDF
ICSEM_maryamjamela__rev2_March11.pdf - Accepted Version
Restricted to Registered users only

Download (235kB)


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.

Item Type: Conference or Workshop Item (Paper)
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments / MOR / COE: Departments > Electrical & Electronic Engineering
Depositing User: Ir Dr Idris Ismail
Date Deposited: 07 Apr 2011 09:22
Last Modified: 19 Jan 2017 08:22

Actions (login required)

View Item
View Item