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ANN-Based Prediction and Optimization of Cooling System in Hotel Rooms

Author

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  • Jin Woo Moon

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

  • Kyungjae Kim

    (DMC R&D Center, Samsung Electronic, Suwon-si 443-742, Gyeonggi-do, Korea)

  • Hyunsuk Min

    (DMC R&D Center, Samsung Electronic, Suwon-si 443-742, Gyeonggi-do, Korea)

Abstract

This study aimed at developing an artificial-neural-network (ANN)-based model that can calculate the required time for restoring the current indoor temperature during the setback period in accommodation buildings to the normal set-point temperature in the cooling season. By applying the calculated time in the control logic, the operation of the cooling system can be predetermined to condition the indoor temperature comfortably in a more energy-efficient manner. Three major steps employing the numerical computer simulation method were conducted for developing an ANN model and testing its prediction performance. In the development process, the initial ANN model was determined to have input neurons that had a significant statistical relationship with the output neuron. In addition, the structure of the ANN model and learning methods were optimized through the parametrical analysis of the prediction performance. Finally, through the performance tests in terms of prediction accuracy, the optimized ANN model presented a lower mean biased error (MBE) rate between the simulation and prediction results under generally accepted levels. Thus, the developed ANN model was proven to have the potential to be applied to thermal control logic.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:10:p:10775-10795:d:56502
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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. Ben-Nakhi, Abdullatif E. & Mahmoud, Mohamed A., 2002. "Energy conservation in buildings through efficient A/C control using neural networks," Applied Energy, Elsevier, vol. 73(1), pages 5-23, September.
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    Cited by:

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    2. Germán Ramos Ruiz & Eva Lucas Segarra & Carlos Fernández Bandera, 2018. "Model Predictive Control Optimization via Genetic Algorithm Using a Detailed Building Energy Model," Energies, MDPI, vol. 12(1), pages 1-18, December.
    3. 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.
    4. Savadkoohi, Marjan & Macarulla, Marcel & Casals, Miquel, 2023. "Facilitating the implementation of neural network-based predictive control to optimize building heating operation," Energy, Elsevier, vol. 263(PB).
    5. Chanuk Lee & Dong Eun Jung & Donghoon Lee & Kee Han Kim & Sung Lok Do, 2021. "Prediction Performance Analysis of Artificial Neural Network Model by Input Variable Combination for Residential Heating Loads," Energies, MDPI, vol. 14(3), pages 1-19, February.
    6. 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.
    7. Dong Eun Jung & Chanuk Lee & Kee Han Kim & Sung Lok Do, 2020. "Development of a Predictive Model for a Photovoltaic Module’s Surface Temperature," Energies, MDPI, vol. 13(15), pages 1-18, August.
    8. Germán Ramos Ruiz & Carlos Fernández Bandera, 2017. "Validation of Calibrated Energy Models: Common Errors," Energies, MDPI, vol. 10(10), pages 1-19, October.
    9. López-Pérez, Luis Adrián & Flores-Prieto, José Jassón, 2023. "Adaptive thermal comfort approach to save energy in tropical climate educational building by artificial intelligence," Energy, Elsevier, vol. 263(PA).
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