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Article: Development of an enthalpy and carbon dioxide based demand control ventilation for indoor air quality and energy saving with neural network control

TitleDevelopment of an enthalpy and carbon dioxide based demand control ventilation for indoor air quality and energy saving with neural network control
Authors
KeywordsDemand control ventilation
Energy saving
Enthalpy
Indoor air quality
Neural network
CO 2
Issue Date2004
Citation
Indoor and Built Environment, 2004, v. 13, n. 6, p. 463-475 How to Cite?
AbstractAn enthalpy and carbon dioxide level based demand control ventilation (EDCV) algorithm has been developed. This takes into account both the indoor occupancy level and the energy content of the fresh air and return air while controlling the fresh air supply. It has been applied under various operating conditions to ensure that the most effective control strategy was used. A back propagation (BP) neural network was used to tune the proportional, integral and differential (PID) parameters in order to obtain a good control performance. Experiments were conducted in a medium-sized lecture theatre to verify the performance of the developed EDCV algorithm in a real application. The results showed that acceptable indoor air quality could be obtained with less energy consumption. Under the optimum experimental conditions, about 15.4% of the total cooling energy was saved. The control performance was found to be good with the PID parameters tuned via the neural network.
Persistent Identifierhttp://hdl.handle.net/10722/255876
ISSN
2015 Impact Factor: 0.943
2015 SCImago Journal Rankings: 0.522

 

DC FieldValueLanguage
dc.contributor.authorChao, Christopher Y.H.-
dc.contributor.authorHu, U. S.-
dc.date.accessioned2018-07-16T06:13:55Z-
dc.date.available2018-07-16T06:13:55Z-
dc.date.issued2004-
dc.identifier.citationIndoor and Built Environment, 2004, v. 13, n. 6, p. 463-475-
dc.identifier.issn1420-326X-
dc.identifier.urihttp://hdl.handle.net/10722/255876-
dc.description.abstractAn enthalpy and carbon dioxide level based demand control ventilation (EDCV) algorithm has been developed. This takes into account both the indoor occupancy level and the energy content of the fresh air and return air while controlling the fresh air supply. It has been applied under various operating conditions to ensure that the most effective control strategy was used. A back propagation (BP) neural network was used to tune the proportional, integral and differential (PID) parameters in order to obtain a good control performance. Experiments were conducted in a medium-sized lecture theatre to verify the performance of the developed EDCV algorithm in a real application. The results showed that acceptable indoor air quality could be obtained with less energy consumption. Under the optimum experimental conditions, about 15.4% of the total cooling energy was saved. The control performance was found to be good with the PID parameters tuned via the neural network.-
dc.languageeng-
dc.relation.ispartofIndoor and Built Environment-
dc.subjectDemand control ventilation-
dc.subjectEnergy saving-
dc.subjectEnthalpy-
dc.subjectIndoor air quality-
dc.subjectNeural network-
dc.subjectCO 2-
dc.titleDevelopment of an enthalpy and carbon dioxide based demand control ventilation for indoor air quality and energy saving with neural network control-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1177/1420326X05047375-
dc.identifier.scopuseid_2-s2.0-10844288841-
dc.identifier.volume13-
dc.identifier.issue6-
dc.identifier.spage463-
dc.identifier.epage475-

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