Graham Kendall
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Professor Graham Kendall

Professor Graham Kendall is the Provost and CEO of The University of Nottingham Malaysia Campus (UNMC). He is also a Pro-Vice Chancellor of the University of Nottingham.

He is a Director of MyResearch Sdn Bhd, Crops for the Future Sdn Bhd. and Nottingham Green Technologies Sdn Bhd. He is a Fellow of the British Computer Society (FBCS) and a Fellow of the Operational Research Society (FORS).

He has published over 230 peer reviewed papers. He is an Associate Editor of 10 journals and the Editor-in-Chief of the IEEE Transactions of Computational Intelligence and AI in Games.

News

A Conversation article celebrating Pi
http://bit.ly/1DXuXbV
How are university examinations scheduled?
http://bit.ly/1z0pG4s

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

When Sports Rules Go Awry: How TheConversation led to a collaborative paper

Publication(s)

Monte Carlo hyper-heuristics for examination timetabling
http://bit.ly/1mlqSFO
Handling diversity in evolutionary multiobjective optimization
http://bit.ly/hN8VPE
Hybridising heuristics within an estimation distribution algorithm for examination timetabling
http://bit.ly/1Plbd56
A Game Theoretic Approach for Taxi Scheduling Problem with Street Hailing
http://bit.ly/1hBsesZ

Graham Kendall: Details of Requested Publication


Citation

Barteczko-Hibbert, C; Gillott, M and Kendall, G An artificial neural network for predicting domestic hot water characteristics. International Journal of Low-Carbon Technologies, 4 (2): 112-119, 2009.


Abstract

Domestic hot water (DHW) in the UK accounts for ~7.5% of all energy use. For manufacturers of heating and hot water appliances to be in a position to respond to patterns of demand a full understanding of the effect of user-defined DHW profiles, different DHW systems and heating technologies are essential. This paper presents the prediction of the temperature characteristics of drawn DHW using artificial neural networks (NNs). We demonstrate whether, based on one NN model, different hot water system temperature loads can be accurately predicted. Two NN models were constructed and examined on a total of three systems. Both models trained on their associated systems produced errors of <11%; however, both NN models, when presented with unseen systems, produced large single errors. NN model 2 gave the lowest error when compared with NN model 1.


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The doi for this publication is 10.1093/ijlct/ctp010 You can link directly to the original paper, via the doi, from here

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Bibtex

@ARTICLE{bgk2009a, author = {C. Barteczko-Hibbert and M. Gillott and G. Kendall},
title = {An artificial neural network for predicting domestic hot water characteristics},
journal = {International Journal of Low-Carbon Technologies},
year = {2009},
volume = {4},
pages = {112-119},
number = {2},
abstract = {Domestic hot water (DHW) in the UK accounts for ~7.5% of all energy use. For manufacturers of heating and hot water appliances to be in a position to respond to patterns of demand a full understanding of the effect of user-defined DHW profiles, different DHW systems and heating technologies are essential. This paper presents the prediction of the temperature characteristics of drawn DHW using artificial neural networks (NNs). We demonstrate whether, based on one NN model, different hot water system temperature loads can be accurately predicted. Two NN models were constructed and examined on a total of three systems. Both models trained on their associated systems produced errors of <11%; however, both NN models, when presented with unseen systems, produced large single errors. NN model 2 gave the lowest error when compared with NN model 1.},
doi = {10.1093/ijlct/ctp010},
issn = {1748-1317},
keywords = {Modelling, domestic hot water, artificial neural networks, random profiles},
owner = {gxk},
timestamp = {2010.10.12} }