Energies 2013, 6(7), 3548-3570; doi:10.3390/en6073548
Article

Determining Adaptability Performance of Artificial Neural Network-Based Thermal Control Logics for Envelope Conditions in Residential Buildings

1 Department of Building & Plant Engineering, Hanbat National University, Daejeon 305-719, Korea 2 School of Architecture, Design & Planning, University of Kansas, Lawrence, KS 66045, USA 3 Department of Interior Architecture & Built Environment, Yonsei University, Seoul 120-749, Korea
* Author to whom correspondence should be addressed.
Received: 17 June 2013; in revised form: 4 July 2013 / Accepted: 4 July 2013 / Published: 18 July 2013
(This article belongs to the Special Issue Energy Efficient Building Design 2013)
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Abstract: This 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.
Keywords: artificial neural network; thermal control logic; thermal performance; envelope insulation; ratio of window to wall; thermal condition

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MDPI and ACS Style

Moon, J.W.; Chang, J.D.; Kim, S. Determining Adaptability Performance of Artificial Neural Network-Based Thermal Control Logics for Envelope Conditions in Residential Buildings. Energies 2013, 6, 3548-3570.

AMA Style

Moon JW, Chang JD, Kim S. Determining Adaptability Performance of Artificial Neural Network-Based Thermal Control Logics for Envelope Conditions in Residential Buildings. Energies. 2013; 6(7):3548-3570.

Chicago/Turabian Style

Moon, Jin W.; Chang, Jae D.; Kim, Sooyoung. 2013. "Determining Adaptability Performance of Artificial Neural Network-Based Thermal Control Logics for Envelope Conditions in Residential Buildings." Energies 6, no. 7: 3548-3570.

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