IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i8p2097-d349048.html
   My bibliography  Save this article

An Online Grey-Box Model Based on Unscented Kalman Filter to Predict Temperature Profiles in Smart Buildings

Author

Listed:
  • Marco Massano

    (Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, 10129 Torino, Italy
    Energy Center Lab, Politecnico di Torino, 10129 Torino, Italy)

  • Edoardo Patti

    (Energy Center Lab, Politecnico di Torino, 10129 Torino, Italy
    Department of Control and Computer Engineering, Politecnico di Torino, 10129 Torino, Italy)

  • Enrico Macii

    (Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, 10129 Torino, Italy)

  • Andrea Acquaviva

    (Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, Università di Bologna, 40126 Bologna, Italy)

  • Lorenzo Bottaccioli

    (Energy Center Lab, Politecnico di Torino, 10129 Torino, Italy
    Department of Control and Computer Engineering, Politecnico di Torino, 10129 Torino, Italy)

Abstract

Nearly 40% of primary energy consumption is related to the usage of energy in Buildings. Energy-related data such as indoor air temperature and power consumption of heating/cooling systems can be now collected due to the widespread diffusion of Internet-of-Things devices. Such energy data can be used (i) to train data-driven models than learn the thermal properties of buildings and (ii) to predict indoor temperature evolution. In this paper, we present a Grey-box model to estimate thermal dynamics in buildings based on Unscented Kalman Filter and thermal network representation. The proposed methodology has been applied in two different buildings with two different thermal network discretizations to test its accuracy in indoor air temperature prediction. Due to a lack of a real-world data sampled by Internet of Things (IoT) devices, a realistic data-set has been generated using the software Energy+ , by referring to real industrial building models. Results on synthetic and realistic data show the accuracy of the proposed methodology in predicting indoor temperature trends up to the next 24 h with a maximum error lower than 2 °C, considering one year of data with different weather conditions.

Suggested Citation

  • Marco Massano & Edoardo Patti & Enrico Macii & Andrea Acquaviva & Lorenzo Bottaccioli, 2020. "An Online Grey-Box Model Based on Unscented Kalman Filter to Predict Temperature Profiles in Smart Buildings," Energies, MDPI, vol. 13(8), pages 1-17, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:8:p:2097-:d:349048
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/8/2097/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/8/2097/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Siano, Pierluigi, 2014. "Demand response and smart grids—A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 461-478.
    2. Deuk-Woo Kim & Cheol-Soo Park, 2017. "Application of Kalman Filter for Estimating a Process Disturbance in a Building Space," Sustainability, MDPI, vol. 9(10), pages 1-17, October.
    3. Kensby, Johan & Trüschel, Anders & Dalenbäck, Jan-Olof, 2015. "Potential of residential buildings as thermal energy storage in district heating systems – Results from a pilot test," Applied Energy, Elsevier, vol. 137(C), pages 773-781.
    4. Lizana, Jesus & Friedrich, Daniel & Renaldi, Renaldi & Chacartegui, Ricardo, 2018. "Energy flexible building through smart demand-side management and latent heat storage," Applied Energy, Elsevier, vol. 230(C), pages 471-485.
    5. Ali Bagheri & Véronique Feldheim & Christos S. Ioakimidis, 2018. "On the Evolution and Application of the Thermal Network Method for Energy Assessments in Buildings," Energies, MDPI, vol. 11(4), pages 1-20, April.
    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. Guelpa, E. & Capone, M. & Sciacovelli, A. & Vasset, N. & Baviere, R. & Verda, V., 2023. "Reduction of supply temperature in existing district heating: A review of strategies and implementations," Energy, Elsevier, vol. 262(PB).
    2. Xu, Tianhao & Gunasekara, Saman Nimali & Chiu, Justin Ningwei & Palm, Björn & Sawalha, Samer, 2020. "Thermal behavior of a sodium acetate trihydrate-based PCM: T-history and full-scale tests," Applied Energy, Elsevier, vol. 261(C).
    3. Lukas Lundström & Jan Akander, 2019. "Bayesian Calibration with Augmented Stochastic State-Space Models of District-Heated Multifamily Buildings," Energies, MDPI, vol. 13(1), pages 1-28, December.
    4. Zhang, Yichi & Johansson, Pär & Kalagasidis, Angela Sasic, 2021. "Techno-economic assessment of thermal energy storage technologies for demand-side management in low-temperature individual heating systems," Energy, Elsevier, vol. 236(C).
    5. Guelpa, Elisa, 2021. "Impact of thermal masses on the peak load in district heating systems," Energy, Elsevier, vol. 214(C).
    6. Erdinc, Ozan, 2014. "Economic impacts of small-scale own generating and storage units, and electric vehicles under different demand response strategies for smart households," Applied Energy, Elsevier, vol. 126(C), pages 142-150.
    7. Villa-Arrieta, Manuel & Sumper, Andreas, 2018. "A model for an economic evaluation of energy systems using TRNSYS," Applied Energy, Elsevier, vol. 215(C), pages 765-777.
    8. McPherson, Madeleine & Stoll, Brady, 2020. "Demand response for variable renewable energy integration: A proposed approach and its impacts," Energy, Elsevier, vol. 197(C).
    9. 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.
    10. Haidar, Ahmed M.A. & Muttaqi, Kashem & Sutanto, Danny, 2015. "Smart Grid and its future perspectives in Australia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1375-1389.
    11. Xu, Xiaojing & Chen, Chien-fei, 2019. "Energy efficiency and energy justice for U.S. low-income households: An analysis of multifaceted challenges and potential," Energy Policy, Elsevier, vol. 128(C), pages 763-774.
    12. Meyabadi, A. Fattahi & Deihimi, M.H., 2017. "A review of demand-side management: Reconsidering theoretical framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 367-379.
    13. Gao, Datong & Zhao, Bin & Kwan, Trevor Hocksun & Hao, Yong & Pei, Gang, 2022. "The spatial and temporal mismatch phenomenon in solar space heating applications: status and solutions," Applied Energy, Elsevier, vol. 321(C).
    14. Tang, Daogui & Fang, Yi-Ping & Zio, Enrico, 2023. "Vulnerability analysis of demand-response with renewable energy integration in smart grids to cyber attacks and online detection methods," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    15. Hao, Ran & Lu, Tianguang & Ai, Qian & Wang, Zhe & Wang, Xiaolong, 2020. "Distributed online learning and dynamic robust standby dispatch for networked microgrids," Applied Energy, Elsevier, vol. 274(C).
    16. Baloglu, Ulas Baran & Demir, Yakup, 2018. "Lightweight privacy-preserving data aggregation scheme for smart grid metering infrastructure protection," International Journal of Critical Infrastructure Protection, Elsevier, vol. 22(C), pages 16-24.
    17. Ke Yan & Xudong Wang & Yang Du & Ning Jin & Haichao Huang & Hangxia Zhou, 2018. "Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy," Energies, MDPI, vol. 11(11), pages 1-15, November.
    18. Sadeghianpourhamami, N. & Demeester, T. & Benoit, D.F. & Strobbe, M. & Develder, C., 2016. "Modeling and analysis of residential flexibility: Timing of white good usage," Applied Energy, Elsevier, vol. 179(C), pages 790-805.
    19. Laura Canale & Anna Rita Di Fazio & Mario Russo & Andrea Frattolillo & Marco Dell’Isola, 2021. "An Overview on Functional Integration of Hybrid Renewable Energy Systems in Multi-Energy Buildings," Energies, MDPI, vol. 14(4), pages 1-33, February.
    20. Ponce, Pedro & Polasko, Kenneth & Molina, Arturo, 2016. "End user perceptions toward smart grid technology: Acceptance, adoption, risks, and trust," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 587-598.

    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:gam:jeners:v:13:y:2020:i:8:p:2097-:d:349048. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.