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Prediction of Cooling Energy Consumption in Hotel Building Using Machine Learning Techniques

Author

Listed:
  • Marek Borowski

    (Faculty of Mining and Geoengineering, AGH University of Science and Technology, 30-059 Kraków, Poland)

  • Klaudia Zwolińska

    (Faculty of Mining and Geoengineering, AGH University of Science and Technology, 30-059 Kraków, Poland)

Abstract

The diversification of energy sources in buildings and the interdependence as well as communication between HVAC installations in the building have resulted in the growing interest in energy load prediction systems that enable proper management of energy resources. In addition, energy storage and the creation of energy buffers are also important in terms of proper resource management, for which it is necessary to correctly determine energy consumption over time. It is obvious that the consumption of cooling energy depends on meteorological conditions. Knowing the parameters of the outside air and the number of users, it is, therefore, possible to determine the hourly energy consumption of a cooling system in a building with some accuracy. The article presents models of cooling energy prediction in summer for a hotel building in southern Poland. The paper presents two methods that are often used for energy prediction: neural networks and support vector machines. Meteorological data, time data, and occupancy level were used as input parameters. Based on the collected input and output data, various configurations were tested to identify the model with the best accuracy. As the analysis showed, higher prediction accuracy was obtained thanks to the use of neural networks. The best of the proposed models was characterized by the WAPE and CV coefficients of 19.93% and 27.03%, respectively.

Suggested Citation

  • Marek Borowski & Klaudia Zwolińska, 2020. "Prediction of Cooling Energy Consumption in Hotel Building Using Machine Learning Techniques," Energies, MDPI, vol. 13(23), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6226-:d:451555
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    References listed on IDEAS

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    3. Koschwitz, D. & Frisch, J. & van Treeck, C., 2018. "Data-driven heating and cooling load predictions for non-residential buildings based on support vector machine regression and NARX Recurrent Neural Network: A comparative study on district scale," Energy, Elsevier, vol. 165(PA), pages 134-142.
    4. Marek Borowski & Piotr Mazur & Sławosz Kleszcz & Klaudia Zwolińska, 2020. "Energy Monitoring in a Heating and Cooling System in a Building Based on the Example of the Turówka Hotel," Energies, MDPI, vol. 13(8), pages 1-20, April.
    5. Wei, Yixuan & Xia, Liang & Pan, Song & Wu, Jinshun & Zhang, Xingxing & Han, Mengjie & Zhang, Weiya & Xie, Jingchao & Li, Qingping, 2019. "Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks," Applied Energy, Elsevier, vol. 240(C), pages 276-294.
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    Cited by:

    1. Gao, Zhikun & Yang, Siyuan & Yu, Junqi & Zhao, Anjun, 2024. "Hybrid forecasting model of building cooling load based on combined neural network," Energy, Elsevier, vol. 297(C).
    2. Marek Borowski, 2022. "Hotel Adapted to the Requirements of an nZEB Building—Thermal Energy Performance and Assessment of Energy Retrofit Plan," Energies, MDPI, vol. 15(17), pages 1-17, August.
    3. Dongsu Kim & Jongman Lee & Sunglok Do & Pedro J. Mago & Kwang Ho Lee & Heejin Cho, 2022. "Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends," Energies, MDPI, vol. 15(19), pages 1-30, October.

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