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Modeling and Optimizing a Chiller System Using a Machine Learning Algorithm

Author

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  • Jee-Heon Kim

    (Eco-System Research Center, Gachon University, Seongnam 13120, Korea)

  • Nam-Chul Seong

    (Eco-System Research Center, Gachon University, Seongnam 13120, Korea)

  • Wonchang Choi

    (Department of Architectural Engineering, Gachon University, Seongnam 13120, Korea)

Abstract

This study was conducted to develop an energy consumption model of a chiller in a heating, ventilation, and air conditioning system using a machine learning algorithm based on artificial neural networks. The proposed chiller energy consumption model was evaluated for accuracy in terms of input layers that include the number of input variables, amount (proportion) of training data, and number of neurons. A standardized reference building was also modeled to generate operational data for the chiller system during extended cooling periods (warm weather months). The prediction accuracy of the chiller’s energy consumption was improved by increasing the number of input variables and adjusting the proportion of training data. By contrast, the effect of the number of neurons on the prediction accuracy was insignificant. The developed chiller model was able to predict energy consumption with 99.07% accuracy based on eight input variables, 60% training data, and 12 neurons.

Suggested Citation

  • Jee-Heon Kim & Nam-Chul Seong & Wonchang Choi, 2019. "Modeling and Optimizing a Chiller System Using a Machine Learning Algorithm," Energies, MDPI, vol. 12(15), pages 1-13, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:15:p:2860-:d:251498
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    References listed on IDEAS

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    1. Serafín Alonso & Antonio Morán & Miguel Ángel Prada & Perfecto Reguera & Juan José Fuertes & Manuel Domínguez, 2019. "A Data-Driven Approach for Enhancing the Efficiency in Chiller Plants: A Hospital Case Study," Energies, MDPI, vol. 12(5), pages 1-28, March.
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    5. Zina Boussaada & Octavian Curea & Ahmed Remaci & Haritza Camblong & Najiba Mrabet Bellaaj, 2018. "A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation," Energies, MDPI, vol. 11(3), pages 1-21, March.
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    Cited by:

    1. Wang, Peng & Sun, Junqing & Yoon, Sungmin & Zhao, Liang & Liang, Ruobing, 2024. "A global optimization method for data center air conditioning water systems based on predictive optimization control," Energy, Elsevier, vol. 295(C).
    2. Chen, Zhiwen & Deng, Qiao & Ren, Hao & Zhao, Zhengrun & Peng, Tao & Yang, Chunhua & Gui, Weihua, 2022. "A new energy consumption prediction method for chillers based on GraphSAGE by combining empirical knowledge and operating data," Applied Energy, Elsevier, vol. 310(C).
    3. Guo, Fangzhou & Li, Ao & Yue, Bao & Xiao, Ziwei & Xiao, Fu & Yan, Rui & Li, Anbang & Lv, Yan & Su, Bing, 2024. "Improving the out-of-sample generalization ability of data-driven chiller performance models using physics-guided neural network," Applied Energy, Elsevier, vol. 354(PA).
    4. Wangqi Xiong & Jiandong Wang, 2020. "Minimizing Power Consumption of an Experimental HVAC System Based on Parallel Grid Searching," Energies, MDPI, vol. 13(8), pages 1-18, April.
    5. Jaroslaw Krzywanski, 2019. "A General Approach in Optimization of Heat Exchangers by Bio-Inspired Artificial Intelligence Methods," Energies, MDPI, vol. 12(23), pages 1-32, November.
    6. Tiezhu Sun & Xiaojun Huang & Caihang Liang & Riming Liu & Xiang Huang, 2022. "Prediction and Analysis of Dew Point Indirect Evaporative Cooler Performance by Artificial Neural Network Method," Energies, MDPI, vol. 15(13), pages 1-14, June.
    7. Jee-Heon Kim & Nam-Chul Seong & Wonchang Choi, 2020. "Forecasting the Energy Consumption of an Actual Air Handling Unit and Absorption Chiller Using ANN Models," Energies, MDPI, vol. 13(17), pages 1-12, August.
    8. Yudong Xia & Shu Jiangzhou & Xuejun Zhang & Zhao Zhang, 2020. "Steady-State Performance Prediction for a Variable Speed Direct Expansion Air Conditioning System Using a White-Box Based Modeling Approach," Energies, MDPI, vol. 13(18), pages 1-17, September.
    9. Vlad Mureșan & Mihaela-Ligia Ungureșan & Mihail Abrudean & Honoriu Vălean & Iulia Clitan & Roxana Motorga & Emilian Ceuca & Marius Fișcă, 2021. "AI versus Classic Methods in Modelling Isotopic Separation Processes: Efficiency Comparison," Mathematics, MDPI, vol. 9(23), pages 1-31, November.
    10. Chun-Wei Chen & Chun-Chang Li & Chen-Yu Lin, 2020. "Combine Clustering and Machine Learning for Enhancing the Efficiency of Energy Baseline of Chiller System," Energies, MDPI, vol. 13(17), pages 1-20, August.
    11. Hou, D. & Evins, R., 2024. "A protocol for developing and evaluating neural network-based surrogate models and its application to building energy prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    12. Jee-Heon Kim & Nam-Chul Seong & Wonchang Choi, 2019. "Cooling Load Forecasting via Predictive Optimization of a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model," Sustainability, MDPI, vol. 11(23), pages 1-13, November.
    13. Junqi Wang & Rundong Liu & Linfeng Zhang & Hussain Syed ASAD & Erlin Meng, 2019. "Triggering Optimal Control of Air Conditioning Systems by Event-Driven Mechanism: Comparing Direct and Indirect Approaches," Energies, MDPI, vol. 12(20), pages 1-20, October.
    14. Manuel R. Arahal & Manuel G. Ortega & Manuel G. Satué, 2021. "Chiller Load Forecasting Using Hyper-Gaussian Nets," Energies, MDPI, vol. 14(12), pages 1-15, June.

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