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Prediction of Hourly Air-Conditioning Energy Consumption in Office Buildings Based on Gaussian Process Regression

Author

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  • Yayuan Feng

    (Department of Civil Engineering, Ningbo University, Ningbo 315211, China)

  • Youxian Huang

    (Bartlett School of Environment, Energy and Resources, University College London, London WC1E 6BT, UK)

  • Haifeng Shang

    (Ningbo Construction Data and Archives Management Center, Ningbo 315040, China)

  • Junwei Lou

    (Ningbo Construction Data and Archives Management Center, Ningbo 315040, China)

  • Ala deen Knefaty

    (Ningbo Aishi Architectural Design Co., Ltd., No. 58, Qizha Street, Haishu District, Ningbo 215171, China)

  • Jian Yao

    (Department of Architecture, Ningbo University, Ningbo 315211, China)

  • Rongyue Zheng

    (Department of Civil Engineering, Ningbo University, Ningbo 315211, China)

Abstract

Accurate prediction of air-conditioning energy consumption in buildings is of great help in reducing building energy consumption. Nowadays, most research efforts on predictive models are based on large samples, while short-term prediction with one-month or less-than-one-month training sets receives less attention due to data uncertainty and unavailability for application in practice. This paper takes a government office building in Ningbo as a case study. The hourly HVAC system energy consumption is obtained through the Ningbo Building Energy Consumption Monitoring Platform, and the meteorological data are obtained from the meteorological station of Ningbo city. This study utilizes a Gaussian process regression with the help of a 12 × 12 grid search and prediction processing to predict short-term hourly building HVAC system energy consumption by using meteorological variables and short-term building HVAC energy consumption data. The accuracy R 2 of the optimal Gaussian process regression model obtained is 0.9917 and 0.9863, and the CV-RMSE is 0.1035 and 0.1278, respectively, for model testing and short-term HVAC system energy consumption prediction. For short-term HVAC system energy consumption, the NMBE is 0.0575, which is more accurate than the standard of ASHRAE, indicating that it can be applied in practical energy predictions.

Suggested Citation

  • Yayuan Feng & Youxian Huang & Haifeng Shang & Junwei Lou & Ala deen Knefaty & Jian Yao & Rongyue Zheng, 2022. "Prediction of Hourly Air-Conditioning Energy Consumption in Office Buildings Based on Gaussian Process Regression," Energies, MDPI, vol. 15(13), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4626-:d:846797
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    References listed on IDEAS

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    Cited by:

    1. Dalia Mohammed Talat Ebrahim Ali & Violeta Motuzienė & Rasa Džiugaitė-Tumėnienė, 2024. "AI-Driven Innovations in Building Energy Management Systems: A Review of Potential Applications and Energy Savings," Energies, MDPI, vol. 17(17), pages 1-35, August.

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