Determining adaptability performance of artificial neural network-based thermal control logics for envelope conditions in residential buildings

Energies

Volumn 6, Issue 7, 2013, Pages 3548-3570

  Moon, Jin Woo ^a   Chang, Jae D ^b   Kim, Sooyoung ^c  

^a Hanbat National University   (South Korea)

^b UNIVERSITY OF KANSAS   (United States)

^c Yonsei University   (South Korea)

Author keywords

Artificial neural network; Envelope insulation; Ratio of window to wall; Thermal condition; Thermal control logic; Thermal performance

Indexed keywords

BUILDINGS; COMPUTER CIRCUITS; COMPUTER SOFTWARE; HOUSING; HUMIDITY CONTROL; NEURAL NETWORKS; SOFTWARE TESTING; THERMAL INSULATION; THERMAL VARIABLES CONTROL; WALLS (STRUCTURAL PARTITIONS);

PREDICTIVE CONTROL METHODS; RATIO OF WINDOW TO WALL; RESIDENTIAL BUILDING; SINGLE-FAMILY HOUSE; THERMAL CONDITION; THERMAL CONTROL; THERMAL PERFORMANCE; WINDOW-TO-WALL RATIO;

DEEP NEURAL NETWORKS;

EID: 84881496039     PISSN: None     EISSN: 19961073     Source Type: Journal    
DOI: 10.3390/en6073548     Document Type: Article

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