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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jin Woo Moon & Kyung-Il Chin & Sooyoung Kim, 2013. "Optimum Application of Thermal Factors to Artificial Neural Network Models for Improvement of Control Performance in Double Skin-Enveloped Buildings," Energies, MDPI, vol. 6(8), pages 1-23, August.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:8:p:4223-4245:d:28099
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    References listed on IDEAS

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    1. Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
    2. Francesco Asdrubali & Franco Cotana & Antonio Messineo, 2012. "On the Evaluation of Solar Greenhouse Efficiency in Building Simulation during the Heating Period," Energies, MDPI, vol. 5(6), pages 1-17, June.
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    Cited by:

    1. Ma, Nan & Aviv, Dorit & Guo, Hongshan & Braham, William W., 2021. "Measuring the right factors: A review of variables and models for thermal comfort and indoor air quality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    2. Jin Woo Moon & Ji-Hyun Lee & Sooyoung Kim, 2014. "Evaluation of Artificial Neural Network-Based Temperature Control for Optimum Operation of Building Envelopes," Energies, MDPI, vol. 7(11), pages 1-21, November.
    3. David Bienvenido-Huertas & Carlos Rubio-Bellido & Juan Luis Pérez-Ordóñez & Fernando Martínez-Abella, 2019. "Estimating Adaptive Setpoint Temperatures Using Weather Stations," Energies, MDPI, vol. 12(7), pages 1-47, March.

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