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

Does AI have a place in the board room?
http://bit.ly/1DXreuW
The hunt for MH370
http://bit.ly/1DXRLbu

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

MISTA Conference: Plenary Talk (Edmund Burke)

Publication(s)

Studying the Effect that a Linear Transformation has on the Time-Series Prediction Ability of an Evolutionary Neural Network
http://bit.ly/eyLaq2
A hyper-heuristic approach to sequencing by hybridization of DNA sequences
http://bit.ly/1mlNjL6
Backward Induction and Repeated Games With Strategy Constraints: An Inspiration From the Surprise Exam Paradox
http://bit.ly/1ib50Nd
Frequency analysis for dendritic cell population tuning
http://bit.ly/go1Ihk

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} }