Next Article in Journal
Analysis and Simulation of Fault Characteristics of Power Switch Failures in Distribution Electronic Power Transformers
Next Article in Special Issue
Assessment of Seasonal Energy Efficiency Strategies of a Double Skin Façade in a Monsoon Climate Region
Previous Article in Journal
Effect of Mixing Driven by Siphon Flow: Parallel Experiments Using the Anaerobic Reactors with Different Mixing Modes
Previous Article in Special Issue
Thermal Efficiency Comparison of Borehole Heat Exchangers with Different Drillhole Diameters
Article Menu

Export Article

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

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

1
Department of Building & Plant Engineering, Hanbat National University, Daejeon 305-719, Korea
2
Department of Architectural Engineering, Hanbat National University, Daejeon 305-719, Korea
3
Department of Interior Architecture & Built Environment, Yonsei University, Seoul 120-749, Korea
*
Author to whom correspondence should be addressed.
Received: 4 June 2013 / Revised: 4 August 2013 / Accepted: 5 August 2013 / Published: 19 August 2013
(This article belongs to the Special Issue Energy Efficient Building Design 2013)

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. View Full-Text
Keywords: double skin envelope; temperature control logic; artificial neural network; predictive and adaptive controls; model optimization double skin envelope; temperature control logic; artificial neural network; predictive and adaptive controls; model optimization
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top