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

Author

Listed:
  • Jin Woo Moon

    (Department of Building & Plant Engineering, Hanbat National University, Daejeon 305-719, Korea)

  • Jae D. Chang

    (School of Architecture, Design & Planning, University of Kansas, Lawrence, KS 66045, USA)

  • Sooyoung Kim

    (Department of Interior Architecture & Built Environment, Yonsei University, Seoul 120-749, Korea)

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.

Suggested Citation

  • Jin Woo Moon & Jae D. Chang & Sooyoung Kim, 2013. "Determining Adaptability Performance of Artificial Neural Network-Based Thermal Control Logics for Envelope Conditions in Residential Buildings," Energies, MDPI, vol. 6(7), pages 1-23, July.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:7:p:3548-3570:d:27321
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    Citations

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    Cited by:

    1. Biswas, M.A. Rafe & Robinson, Melvin D. & Fumo, Nelson, 2016. "Prediction of residential building energy consumption: A neural network approach," Energy, Elsevier, vol. 117(P1), pages 84-92.
    2. Elnazeer Ali Hamid Abdalla & Perumal Nallagownden & Nursyarizal Bin Mohd Nor & Mohd Fakhizan Romlie & Sabo Miya Hassan, 2018. "An Application of a Novel Technique for Assessing the Operating Performance of Existing Cooling Systems on a University Campus," Energies, MDPI, vol. 11(4), pages 1-24, March.
    3. Mario Collotta & Antonio Messineo & Giuseppina Nicolosi & Giovanni Pau, 2014. "A Dynamic Fuzzy Controller to Meet Thermal Comfort by Using Neural Network Forecasted Parameters as the Input," Energies, MDPI, vol. 7(8), pages 1-30, July.
    4. Otilia Elena Dragomir & Florin Dragomir & Veronica Stefan & Eugenia Minca, 2015. "Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable Sources," Energies, MDPI, vol. 8(11), pages 1-15, November.
    5. 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.
    6. Frederik Ruelens & Sandro Iacovella & Bert J. Claessens & Ronnie Belmans, 2015. "Learning Agent for a Heat-Pump Thermostat with a Set-Back Strategy Using Model-Free Reinforcement Learning," Energies, MDPI, vol. 8(8), pages 1-19, August.

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