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Performance of a Predictive Model for Calculating Ascent Time to a Target Temperature

Author

Listed:
  • Jin Woo Moon

    (School of Architecture and Building Science, Chung-Ang University, Seoul 06974, Korea)

  • Min Hee Chung

    (School of Architecture and Building Science, Chung-Ang University, Seoul 06974, Korea)

  • Hayub Song

    (School of Architecture and Building Science, Chung-Ang University, Seoul 06974, Korea)

  • Se-Young Lee

    (School of Architecture and Building Science, Chung-Ang University, Seoul 06974, Korea)

Abstract

The aim of this study was to develop an artificial neural network (ANN) prediction model for controlling building heating systems. This model was used to calculate the ascent time of indoor temperature from the setback period (when a building was not occupied) to a target setpoint temperature (when a building was occupied). The calculated ascent time was applied to determine the proper moment to start increasing the temperature from the setback temperature to reach the target temperature at an appropriate time. Three major steps were conducted: (1) model development; (2) model optimization; and (3) performance evaluation. Two software programs—Matrix Laboratory (MATLAB) and Transient Systems Simulation (TRNSYS)—were used for model development, performance tests, and numerical simulation methods. Correlation analysis between input variables and the output variable of the ANN model revealed that two input variables (current indoor air temperature and temperature difference from the target setpoint temperature), presented relatively strong relationships with the ascent time to the target setpoint temperature. These two variables were used as input neurons. Analyzing the difference between the simulated and predicted values from the ANN model provided the optimal number of hidden neurons (9), hidden layers (3), moment (0.9), and learning rate (0.9). At the study’s conclusion, the optimized model proved its prediction accuracy with acceptable errors.

Suggested Citation

  • Jin Woo Moon & Min Hee Chung & Hayub Song & Se-Young Lee, 2016. "Performance of a Predictive Model for Calculating Ascent Time to a Target Temperature," Energies, MDPI, vol. 9(12), pages 1-16, December.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:12:p:1090-:d:85618
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    References listed on IDEAS

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    1. Jin Woo Moon & Kyungjae Kim & Hyunsuk Min, 2015. "ANN-Based Prediction and Optimization of Cooling System in Hotel Rooms," Energies, MDPI, vol. 8(10), pages 1-21, September.
    2. 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.
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    Cited by:

    1. Petri Hietaharju & Mika Ruusunen & Kauko Leiviskä, 2018. "A Dynamic Model for Indoor Temperature Prediction in Buildings," Energies, MDPI, vol. 11(6), pages 1-20, June.
    2. Giovanni Pau & Mario Collotta & Antonio Ruano & Jiahu Qin, 2017. "Smart Home Energy Management," Energies, MDPI, vol. 10(3), pages 1-5, March.
    3. Roberto Zanetti Freire & Gerson Henrique dos Santos & Leandro dos Santos Coelho, 2017. "Hygrothermal Dynamic and Mould Growth Risk Predictions for Concrete Tiles by Using Least Squares Support Vector Machines," Energies, MDPI, vol. 10(8), pages 1-16, July.

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