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

Jin Woo Moon, Kyung-Il Chin and Sooyoung Kim
Additional contact information
Jin Woo Moon: Department of Building & Plant Engineering, Hanbat National University, Daejeon 305-719, Korea
Kyung-Il Chin: Department of Architectural Engineering, Hanbat National University, Daejeon 305-719, Korea
Sooyoung Kim: Department of Interior Architecture & Built Environment, Yonsei University, Seoul 120-749, Korea

Energies, 2013, vol. 6, issue 8, 1-23

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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/6/8/4223/pdf (application/pdf)
https://www.mdpi.com/1996-1073/6/8/4223/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:6:y:2013:i:8:p:4223-4245:d:28099

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().