Utilizing artificial neural network to predict energy consumption and thermal comfort level: an indoor swimming pool case study

Yuce, Baris ORCID: https://orcid.org/0000-0002-9937-1535, Li, Haijiang ORCID: https://orcid.org/0000-0001-6326-8133, Rezgui, Yacine ORCID: https://orcid.org/0000-0002-5711-8400, Petri, Ioan ORCID: https://orcid.org/0000-0002-1625-8247, Jayan, Bejay and Yang, Chunfeng 2014. Utilizing artificial neural network to predict energy consumption and thermal comfort level: an indoor swimming pool case study. Energy and Buildings 80 , pp. 45-56. 10.1016/j.enbuild.2014.04.052

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Abstract

This paper presents an Artificial Neural Network (ANN) approach to predict energy consumption and thermal comfort level (represented by Predicted Mean Vote (PMV)) of an indoor swimming pool. In a swimming pool, several environmental and control variables, directly and/or indirectly, affect energy consumption and thermal comfort, rendering difficult the development of a mathematical relationship amongst input and output variables. Thus, an ANN based prediction approach is used to elicit this relationship within a reasonable period of time. This forms the basis of an optimization based control system to evaluate the control parameters in the swimming pool. The proposed approach is implemented for a specific Heating, Ventilation, and Air Conditioning (HVAC) system, based on use cases/scenarios developed in close consultation with site engineers and domain experts. Due to lack of meaningful historical monitored data (from sensors and smart meters), a calibrated simulation model is used to generate large amount of data sets to train the corresponding ANN prediction engine. The trained ANN was then calibrated in real conditions and used as a cost function in an optimization program to help achieve energy saving targets. Several ANN algorithms have been tested and benchmarked leading to the selection, with further tuning, of the best performing ANN algorithm, namely Levenberg-Marquardt training algorithm. The latter was used and achieved good results as demonstrated in the selected case study.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TD Environmental technology. Sanitary engineering
Uncontrolled Keywords:	ANN; Energy Management; Parameter Tuning; PMV; Indoor Swimming Pool
Additional Information:	Online publication date: 9 May 2014.
Publisher:	Elsevier
ISSN:	0378-7788
Date of Acceptance:	26 April 2014
Last Modified:	12 Jul 2024 14:50
URI:	https://orca.cardiff.ac.uk/id/eprint/59559

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