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Field validation study of a time and temperature indexed autoregressive with exogenous (ARX) model for building thermal load prediction

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  • Sarwar, Riasat
  • Cho, Heejin
  • Cox, Sam J.
  • Mago, Pedro J.
  • Luck, Rogelio

Abstract

Building load prediction algorithms are becoming an essential component of building energy technologies as intelligent building technologies are rapidly evolving and require accurate load predictions to make real-time operational decisions. This paper presents a field validation study of an autoregressive with exogenous (ARX) model, indexed with respect to time and temperature, and used for hourly building thermal load prediction with an aim for integration with real time predictive control strategies. Indexing of the ARX model implies that different sets of coefficients are used in the predictive equation depending on different time intervals and temperature ranges. Although many regressive prediction models have been proposed, no field validation has been reported in the literature, which is an essential step before implementation in actual practice. The validation study was carried out using field data from three buildings located in the main campus of Mississippi State University. The proposed model was able to predict hourly thermal load accurately and within the uncertainty bounds of the measured thermal load most of the time. Results also demonstrated that proper indexing of the model allowed it to capture different cooling and heating load profiles and abrupt changes in the load pattern.

Suggested Citation

  • Sarwar, Riasat & Cho, Heejin & Cox, Sam J. & Mago, Pedro J. & Luck, Rogelio, 2017. "Field validation study of a time and temperature indexed autoregressive with exogenous (ARX) model for building thermal load prediction," Energy, Elsevier, vol. 119(C), pages 483-496.
    • Handle: RePEc:eee:energy:v:119:y:2017:i:c:p:483-496
    DOI: 10.1016/j.energy.2016.12.083
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    References listed on IDEAS

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    1. Pedersen, Linda, 2007. "Use of different methodologies for thermal load and energy estimations in buildings including meteorological and sociological input parameters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(5), pages 998-1007, June.
    2. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
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    Cited by:

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    2. Gao, Zhikun & Yu, Junqi & Zhao, Anjun & Hu, Qun & Yang, Siyuan, 2022. "A hybrid method of cooling load forecasting for large commercial building based on extreme learning machine," Energy, Elsevier, vol. 238(PC).
    3. Zhicheng Xiao & Lijuan Yu & Huajun Zhang & Xuetao Zhang & Yixin Su, 2023. "HVAC Load Forecasting Based on the CEEMDAN-Conv1D-BiLSTM-AM Model," Mathematics, MDPI, vol. 11(22), pages 1-24, November.
    4. Ntumba Marc-Alain Mutombo & Bubele Papy Numbi, 2022. "The Development of ARIMA Models for the Clear Sky Beam and Diffuse Optical Depths for HVAC Systems Design Using RTSM: A Case Study of the Umlazi Township Area, South Africa," Sustainability, MDPI, vol. 14(6), pages 1-16, March.
    5. 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.
    6. Suzana Domjan & Sašo Medved & Boštjan Černe & Ciril Arkar, 2019. "Fast Modelling of nZEB Metrics of Office Buildings Built with Advanced Glass and BIPV Facade Structures," Energies, MDPI, vol. 12(16), pages 1-18, August.
    7. Zhang, Lidong & Li, Jiao & Xu, Xiandong & Liu, Fengrui & Guo, Yuanjun & Yang, Zhile & Hu, Tianyu, 2023. "High spatial granularity residential heating load forecast based on Dendrite net model," Energy, Elsevier, vol. 269(C).
    8. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    9. Cox, Sam J. & Kim, Dongsu & Cho, Heejin & Mago, Pedro, 2019. "Real time optimal control of district cooling system with thermal energy storage using neural networks," Applied Energy, Elsevier, vol. 238(C), pages 466-480.
    10. Chung, Won Hee & Gu, Yeong Hyeon & Yoo, Seong Joon, 2022. "District heater load forecasting based on machine learning and parallel CNN-LSTM attention," Energy, Elsevier, vol. 246(C).
    11. Ahmed, Ahmed I. & McLeod, Robert S. & Gustin, Matej, 2021. "Forecasting underheating in dwellings to detect excess winter mortality risks using time series models," Applied Energy, Elsevier, vol. 286(C).

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