IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v321y2022ics0306261922006353.html
   My bibliography  Save this article

Estimating dynamic solar gains from on-site measured data: An ARX modelling approach

Author

Listed:
  • Zhang, Xiang
  • Saelens, Dirk
  • Roels, Staf

Abstract

On-site measured data in combination with statistical methods is used more and more to assess the actual performance of a building and to develop simplified models that can be used in model predictive control, fault detection, and optimization of energy grids. Most of the methods are based on a simplified heat balance of the building. For this heat balance, solar gains, referring to the part of energy supplied by the sun, are a vital factor. Gauging solar gains and their time dependency in practice is, however, challenging. Most models assume the solar aperture of the building as an invariable property, although it is highly dependent on the sun position during the time of the day and year. In this study a statistical modeling method, based on in-situ measurement data, is developed to estimate the time-varying solar gains more precisely. The method integrates basis splines (B-splines) into an ARX model (Auto-regressive with eXogenous input). Verified by white-box model simulation outcomes, it is demonstrated that this B-splines integrated ARX modelling approach can reflect the key information of the dynamic features ofthe solar gainsto a large extent. Most importantly, only a limited size of in-situ measurement data of high-frequency is required in this method, indicating its good efficiency in dynamic solar gain estimation.

Suggested Citation

  • Zhang, Xiang & Saelens, Dirk & Roels, Staf, 2022. "Estimating dynamic solar gains from on-site measured data: An ARX modelling approach," Applied Energy, Elsevier, vol. 321(C).
    • Handle: RePEc:eee:appene:v:321:y:2022:i:c:s0306261922006353
    DOI: 10.1016/j.apenergy.2022.119278
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922006353
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.119278?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yessenia Olazo-Gómez & Héctor Herrada & Sergio Castaño & Jesús Arce & Jesús P. Xamán & María José Jiménez, 2020. "Data-Based RC Dynamic Modelling to Assessing the In-Situ Thermal Performance of Buildings. Analysis of Several Key Aspects in a Simplified Reference Case toward the Application at On-Board Monitoring ," Energies, MDPI, vol. 13(18), pages 1-30, September.
    2. Niu, Jide & Tian, Zhe & Lu, Yakai & Zhao, Hongfang, 2019. "Flexible dispatch of a building energy system using building thermal storage and battery energy storage," Applied Energy, Elsevier, vol. 243(C), pages 274-287.
    3. Kontoleon, K.J., 2015. "Glazing solar heat gain analysis and optimization at varying orientations and placements in aspect of distributed radiation at the interior surfaces," Applied Energy, Elsevier, vol. 144(C), pages 152-164.
    4. Evi Lambie & Dirk Saelens, 2020. "Identification of the Building Envelope Performance of a Residential Building: A Case Study," Energies, MDPI, vol. 13(10), pages 1-28, May.
    5. Claeskens,Gerda & Hjort,Nils Lid, 2008. "Model Selection and Model Averaging," Cambridge Books, Cambridge University Press, number 9780521852258.
    6. Fan, Cheng & Xiao, Fu & Wang, Shengwei, 2014. "Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques," Applied Energy, Elsevier, vol. 127(C), pages 1-10.
    7. Hu, Maomao & Xiao, Fu & Wang, Lingshi, 2017. "Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model," Applied Energy, Elsevier, vol. 207(C), pages 324-335.
    8. Kialashaki, Arash & Reisel, John R., 2013. "Modeling of the energy demand of the residential sector in the United States using regression models and artificial neural networks," Applied Energy, Elsevier, vol. 108(C), pages 271-280.
    9. Deb, C. & Gelder, L.V. & Spiekman, M. & Pandraud, Guillaume & Jack, R. & Fitton, R., 2021. "Measuring the heat transfer coefficient (HTC) in buildings: A stakeholder's survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    10. Ferracuti, Francesco & Fonti, Alessandro & Ciabattoni, Lucio & Pizzuti, Stefano & Arteconi, Alessia & Helsen, Lieve & Comodi, Gabriele, 2017. "Data-driven models for short-term thermal behaviour prediction in real buildings," Applied Energy, Elsevier, vol. 204(C), pages 1375-1387.
    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).
    12. Shamsi, Mohammad Haris & Ali, Usman & Mangina, Eleni & O’Donnell, James, 2020. "A framework for uncertainty quantification in building heat demand simulations using reduced-order grey-box energy models," Applied Energy, Elsevier, vol. 275(C).
    13. Kyung-joo Cho & Dong-woo Cho, 2018. "Solar Heat Gain Coefficient Analysis of a Slim-Type Double Skin Window System: Using an Experimental and a Simulation Method," Energies, MDPI, vol. 11(1), pages 1-17, January.
    14. Marieline Senave & Staf Roels & Stijn Verbeke & Evi Lambie & Dirk Saelens, 2019. "Sensitivity of Characterizing the Heat Loss Coefficient through On-Board Monitoring: A Case Study Analysis," Energies, MDPI, vol. 12(17), pages 1-29, August.
    15. Zhang, Xiang & Rasmussen, Christoffer & Saelens, Dirk & Roels, Staf, 2022. "Time-dependent solar aperture estimation of a building: Comparing grey-box and white-box approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    16. Heidi Paola Díaz-Hernández & Pablo René Torres-Hernández & Karla María Aguilar-Castro & Edgar Vicente Macias-Melo & María José Jiménez, 2020. "Data-Based RC Dynamic Modelling Incorporating Physical Criteria to Obtain the HLC of In-Use Buildings: Application to a Case Study," Energies, MDPI, vol. 13(2), pages 1-22, January.
    17. Li, Yanfei & O'Neill, Zheng & Zhang, Liang & Chen, Jianli & Im, Piljae & DeGraw, Jason, 2021. "Grey-box modeling and application for building energy simulations - A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Xiang & Rasmussen, Christoffer & Saelens, Dirk & Roels, Staf, 2022. "Time-dependent solar aperture estimation of a building: Comparing grey-box and white-box approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    2. 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.
    3. Li, Yanxue & Wang, Zixuan & Xu, Wenya & Gao, Weijun & Xu, Yang & Xiao, Fu, 2023. "Modeling and energy dynamic control for a ZEH via hybrid model-based deep reinforcement learning," Energy, Elsevier, vol. 277(C).
    4. 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.
    5. María José Jiménez & José Alberto Díaz & Antonio Javier Alonso & Sergio Castaño & Manuel Pérez, 2020. "Non-Intrusive Measurements to Incorporate the Air Renovations in Dynamic Models Assessing the In-Situ Thermal Performance of Buildings," Energies, MDPI, vol. 14(1), pages 1-15, December.
    6. Lara Ramadan & Isam Shahrour & Hussein Mroueh & Fadi Hage Chehade, 2021. "Use of Machine Learning Methods for Indoor Temperature Forecasting," Future Internet, MDPI, vol. 13(10), pages 1-18, September.
    7. Yang, Wangwang & Shi, Jing & Li, Shujian & Song, Zhaofang & Zhang, Zitong & Chen, Zexu, 2022. "A combined deep learning load forecasting model of single household resident user considering multi-time scale electricity consumption behavior," Applied Energy, Elsevier, vol. 307(C).
    8. Maltais, Louis-Gabriel & Gosselin, Louis, 2022. "Forecasting of short-term lighting and plug load electricity consumption in single residential units: Development and assessment of data-driven models for different horizons," Applied Energy, Elsevier, vol. 307(C).
    9. Kamel, Ehsan & Sheikh, Shaya & Huang, Xueqing, 2020. "Data-driven predictive models for residential building energy use based on the segregation of heating and cooling days," Energy, Elsevier, vol. 206(C).
    10. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
    11. Jason Runge & Radu Zmeureanu, 2019. "Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review," Energies, MDPI, vol. 12(17), pages 1-27, August.
    12. Kitagawa, Toru & Muris, Chris, 2016. "Model averaging in semiparametric estimation of treatment effects," Journal of Econometrics, Elsevier, vol. 193(1), pages 271-289.
    13. Damianakis, Nikolaos & Mouli, Gautham Ram Chandra & Bauer, Pavol & Yu, Yunhe, 2023. "Assessing the grid impact of Electric Vehicles, Heat Pumps & PV generation in Dutch LV distribution grids," Applied Energy, Elsevier, vol. 352(C).
    14. Chen, Han & Huang, Ye & Shen, Huizhong & Chen, Yilin & Ru, Muye & Chen, Yuanchen & Lin, Nan & Su, Shu & Zhuo, Shaojie & Zhong, Qirui & Wang, Xilong & Liu, Junfeng & Li, Bengang & Tao, Shu, 2016. "Modeling temporal variations in global residential energy consumption and pollutant emissions," Applied Energy, Elsevier, vol. 184(C), pages 820-829.
    15. Fan, Cheng & Sun, Yongjun & Zhao, Yang & Song, Mengjie & Wang, Jiayuan, 2019. "Deep learning-based feature engineering methods for improved building energy prediction," Applied Energy, Elsevier, vol. 240(C), pages 35-45.
    16. Vallianos, Charalampos & Candanedo, José & Athienitis, Andreas, 2023. "Application of a large smart thermostat dataset for model calibration and Model Predictive Control implementation in the residential sector," Energy, Elsevier, vol. 278(PA).
    17. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    18. Mardones, Cristian, 2021. "Ex-post evaluation and cost-benefit analysis of a heater replacement program implemented in southern Chile," Energy, Elsevier, vol. 227(C).
    19. Davide Fiaschi & Andrea Mario Lavezzi & Angela Parenti, 2020. "Deep and Proximate Determinants of the World Income Distribution," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 66(3), pages 677-710, September.
    20. Anatoliy M. Pavlenko & Karolina Sadko, 2023. "Evaluation of Numerical Methods for Predicting the Energy Performance of Windows," Energies, MDPI, vol. 16(3), pages 1-23, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:321:y:2022:i:c:s0306261922006353. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.