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dc.contributor.authorMoon, Jin Woo
dc.contributor.authorChang, Jae D.
dc.contributor.authorKim, Sooyoung
dc.date.accessioned2014-06-19T17:16:50Z
dc.date.available2014-06-19T17:16:50Z
dc.date.issued2013-07-18
dc.identifier.citationJin Woo Moon, Jae D. Chang and Sooyoung Kim. (2013). “Determining Adaptability Performance of Artificial Neural Network-Based Thermal Control Logics for Envelope Conditions in Residential Buildings.” Energies 6(7):3548-3570. http://www.dx.doi.org/10.3390/en6073548
dc.identifier.urihttp://hdl.handle.net/1808/14282
dc.descriptionThis is the publisher's version, also available electronically from http://www.mdpi.com/1996-1073/6/7/3548
dc.description.abstractThis study examines the performance and adaptability of Artificial Neural Network (ANN)-based thermal control strategies for diverse thermal properties of building envelope conditions applied to residential buildings. The thermal performance using two non-ANN-based control logics and two predictive ANN-based control logics was numerically tested using simulation software after validation. The performance tests were conducted for a two-story single-family house for various envelope insulation levels and window-to-wall ratios on the envelopes. The percentages of the period within the targeted ranges for air temperature, humidity and PMV, and the magnitudes of the overshoots and undershoots outside of the targeted comfort range were analyzed for each control logic scheme. The results revealed that the two predictive control logics that employed thermal predictions of the ANN models achieved longer periods of thermal comfort than the non-ANN-based models in terms of the comfort periods and the reductions of the magnitudes of the overshoots and undershoots. The ANN-based models proved their adaptability through accurate control of the thermal conditions in buildings with various architectural variables. The ANN-based predictive control methods demonstrated their potential to create more comfortable thermal conditions in single-family homes compared to non-ANN based control logics.
dc.publisherMDPI
dc.subjectArtificial Neural Network
dc.subjectThermal Control Logic
dc.subjectThermal Performance
dc.subjectEnvelope Insulation
dc.subjectRatio Of Window To Wall
dc.subjectThermal Condition
dc.titleDetermining Adaptability Performance of Artificial Neural Network-Based Thermal Control Logics for Envelope Conditions in Residential Buildings
dc.typeArticle
kusw.kuauthorChang, Jae D.
kusw.kudepartmentArchitecture
kusw.oastatusfullparticipation
dc.identifier.doi10.3390/en6073548
kusw.oaversionScholarly/refereed, publisher version
kusw.oapolicyThis item meets KU Open Access policy criteria.
dc.rights.accessrightsopenAccess


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