relation: https://khub.utp.edu.my/scholars/2080/
title: Adaptive neural network prediction model for energy consumption
creator: Ismail, M.J.
creator: Ibrahim, R.
creator: Ismail, I.
description: 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.
date: 2011
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Ismail, M.J. and Ibrahim, R. and Ismail, I.  (2011) Adaptive neural network prediction model for energy consumption.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-79957536756&doi=10.1109%2fICCRD.2011.5763864&partnerID=40&md5=c91705abba258f3d32746fd9687258b6
relation: 10.1109/ICCRD.2011.5763864
identifier: 10.1109/ICCRD.2011.5763864