Energies 2013, 6(8), 4223-4245; doi:10.3390/en6084223
Article

Optimum Application of Thermal Factors to Artificial Neural Network Models for Improvement of Control Performance in Double Skin-Enveloped Buildings

Received: 4 June 2013; in revised form: 4 August 2013 / Accepted: 5 August 2013 / Published: 19 August 2013
(This article belongs to the Special Issue Energy Efficient Building Design 2013)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: This study proposes an artificial neural network (ANN)-based thermal control method for buildings with double skin envelopes that has rational relationships between the ANN model input and output. The relationship between the indoor air temperature and surrounding environmental factors was investigated based on field measurement data from an actual building. The results imply that the indoor temperature was not significantly influenced by vertical solar irradiance, but by the outdoor and cavity temperature. Accordingly, a new ANN model developed in this study excluded solar irradiance as an input variable for predicting the future indoor temperature. The structure and learning method of this new ANN model was optimized, followed by the performance tests of a variety of internal and external envelope opening strategies for the heating and cooling seasons. The performance tests revealed that the optimized ANN-based logic yielded better temperature conditions than the non-ANN based logic. This ANN-based logic increased overall comfortable periods and decreased the frequency of overshoots and undershoots out of the thermal comfort range. The ANN model proved that it has the potential to be successfully applied in the temperature control logic for double skin-enveloped buildings. The ANN model, which was proposed in this study, effectively predicted future indoor temperatures for the diverse opening strategies. The ANN-based logic optimally determined the operation of heating and cooling systems as well as opening conditions for the double skin envelopes.
Keywords: double skin envelope; temperature control logic; artificial neural network; predictive and adaptive controls; model optimization
PDF Full-text Download PDF Full-Text [851 KB, uploaded 19 August 2013 15:17 CEST]

Export to BibTeX |
EndNote


MDPI and ACS Style

Moon, J.W.; Chin, K.-I.; Kim, S. Optimum Application of Thermal Factors to Artificial Neural Network Models for Improvement of Control Performance in Double Skin-Enveloped Buildings. Energies 2013, 6, 4223-4245.

AMA Style

Moon JW, Chin K-I, Kim S. Optimum Application of Thermal Factors to Artificial Neural Network Models for Improvement of Control Performance in Double Skin-Enveloped Buildings. Energies. 2013; 6(8):4223-4245.

Chicago/Turabian Style

Moon, Jin W.; Chin, Kyung-Il; Kim, Sooyoung. 2013. "Optimum Application of Thermal Factors to Artificial Neural Network Models for Improvement of Control Performance in Double Skin-Enveloped Buildings." Energies 6, no. 8: 4223-4245.

Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert