IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v9y2017i8p1462-d108703.html

Smart Thermostats: An Experimental Facility to Test Their Capabilities and Savings Potential

Author

Listed:
  • Sergio Bustamante

    (School of Industrial Engineering, University of Cantabria, Av. Los Castros s/n, 39005 Cantabria, Spain)

  • Pablo Castro

    (School of Industrial Engineering, University of Cantabria, Av. Los Castros s/n, 39005 Cantabria, Spain)

  • Alberto Laso

    (School of Industrial Engineering, University of Cantabria, Av. Los Castros s/n, 39005 Cantabria, Spain)

  • Mario Manana

    (School of Industrial Engineering, University of Cantabria, Av. Los Castros s/n, 39005 Cantabria, Spain)

  • Alberto Arroyo

    (School of Industrial Engineering, University of Cantabria, Av. Los Castros s/n, 39005 Cantabria, Spain)

Abstract

The European Commission has explained how heating and cooling in buildings and industry account for half of the energy consumption of the EU. Several studies explain how to achieve an energy saving at home, and the use of smart thermostats will help to reduce energy consumption while increasing the efficiency of households. In this article, a comparative evaluation was carried out between four smart thermostats that are now on the market, whose characteristics vary in terms of price, precision of measurements and set temperature, algorithms, etc. A thermal test chamber was designed and constructed from a refrigerator, a thermal blanket, a Raspberry Pi and the necessary electronic components for its control and data collection. From the tests carried out in the thermal chamber, data on the operation of the four thermostats such as the maintenance and the anticipation of the setpoint temperature, were obtained. It was necessary to run the system enough times for each thermostat to memorize the housing characteristics, such as its inertia and its thermal insulation. This would also allow for the generation of a better algorithm to regulate the temperature, which would create a lower oscillation with respect to the setpoint temperature. The learning of the thermostats was not demonstrated and for the anticipation mode it was seen that the thermostats failed to improve or learn in this aspect, as they did not improve the start-up times of the heating system, with the consequent increase in energy consumption.

Suggested Citation

  • Sergio Bustamante & Pablo Castro & Alberto Laso & Mario Manana & Alberto Arroyo, 2017. "Smart Thermostats: An Experimental Facility to Test Their Capabilities and Savings Potential," Sustainability, MDPI, vol. 9(8), pages 1-18, August.
  • Handle: RePEc:gam:jsusta:v:9:y:2017:i:8:p:1462-:d:108703
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/9/8/1462/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/9/8/1462/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gabriele Lobaccaro & Salvatore Carlucci & Erica Löfström, 2016. "A Review of Systems and Technologies for Smart Homes and Smart Grids," Energies, MDPI, vol. 9(5), pages 1-33, May.
    2. Sally S. Shahzad & John Brennan & Dimitris Theodossopoulos & Ben Hughes & John Kaiser Calautit, 2016. "Building-Related Symptoms, Energy, and Thermal Control in the Workplace: Personal and Open Plan Offices," Sustainability, MDPI, vol. 8(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. Flavio Martins & Maria Fatima Almeida & Rodrigo Calili & Agatha Oliveira, 2020. "Design Thinking Applied to Smart Home Projects: A User-Centric and Sustainable Perspective," Sustainability, MDPI, vol. 12(23), pages 1-27, December.
    2. Gitelman, Lazar & Kozhevnikov, Mikhail & Ditenberg, Maksim, 2024. "Electrification as a factor in replacing hydrocarbon fuel," Energy, Elsevier, vol. 307(C).
    3. Mohamed Massaoudi & Ines Chihi & Lilia Sidhom & Mohamed Trabelsi & Shady S. Refaat & Fakhreddine S. Oueslati, 2021. "Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements," Energies, MDPI, vol. 14(13), pages 1-20, July.
    4. Hafize Nurgul Durmus Senyapar & Ramazan Bayindir, 2023. "The Research Agenda on Smart Grids: Foresights for Social Acceptance," Energies, MDPI, vol. 16(18), pages 1-31, September.
    5. Severinsen, A. & Myrland, Ø., 2022. "ShinyRBase: Near real-time energy saving models using reactive programming," Applied Energy, Elsevier, vol. 325(C).
    6. Chen, Chien-fei & Nelson, Hannah & Xu, Xiaojing & Bonilla, Gregory & Jones, Nicholas, 2021. "Beyond technology adoption: Examining home energy management systems, energy burdens and climate change perceptions during COVID-19 pandemic," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    7. Adnan Ahmad & Asif Khan & Nadeem Javaid & Hafiz Majid Hussain & Wadood Abdul & Ahmad Almogren & Atif Alamri & Iftikhar Azim Niaz, 2017. "An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources," Energies, MDPI, vol. 10(4), pages 1-35, April.
    8. Kalmár, Tünde & Szodrai, Ferenc & Kalmár, Ferenc, 2022. "Experimental study of local effectiveness in the case of balanced mechanical ventilation in small offices," Energy, Elsevier, vol. 244(PA).
    9. Syed Ali Abbas Kazmi & Muhammad Khuram Shahzad & Akif Zia Khan & Dong Ryeol Shin, 2017. "Smart Distribution Networks: A Review of Modern Distribution Concepts from a Planning Perspective," Energies, MDPI, vol. 10(4), pages 1-47, April.
    10. Fei Jiao & Yuqing Lei & Guozheng Peng & Funing Dong & Qing Yang & Wei Liao, 2023. "Grating Spectrum Design and Optimization of GMM-FBG Current Sensor," Energies, MDPI, vol. 16(2), pages 1-12, January.
    11. Amit Shewale & Anil Mokhade & Nitesh Funde & Neeraj Dhanraj Bokde, 2022. "A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes," Energies, MDPI, vol. 15(8), pages 1-34, April.
    12. Aniela Kaminska & Andrzej Ożadowicz, 2018. "Lighting Control Including Daylight and Energy Efficiency Improvements Analysis," Energies, MDPI, vol. 11(8), pages 1-18, August.
    13. Ivan Cibrario Bertolotti, 2025. "A C-Based Framework for Low-Cost Real-Time Embedded Systems," Future Internet, MDPI, vol. 17(6), pages 1-27, June.
    14. Hiroko Nakaoka & Norimichi Suzuki & Akifumi Eguchi & Daisuke Matsuzawa & Chisato Mori, 2022. "Impact of Exposure to Indoor Air Chemicals on Health and the Progression of Building-Related Symptoms: A Case Report," Sustainability, MDPI, vol. 14(21), pages 1-13, November.
    15. Carlos Cruz & Esther Palomar & Ignacio Bravo & Alfredo Gardel, 2020. "Cooperative Demand Response Framework for a Smart Community Targeting Renewables: Testbed Implementation and Performance Evaluation," Energies, MDPI, vol. 13(11), pages 1-20, June.
    16. Simona-Vasilica Oprea & Adela Bâra & Ștefan Preda & Osman Bulent Tor, 2020. "A Smart Adaptive Switching Module Architecture Using Fuzzy Logic for an Efficient Integration of Renewable Energy Sources. A Case Study of a RES System Located in Hulubești, Romania," Sustainability, MDPI, vol. 12(15), pages 1-27, July.
    17. Wang, Huiyuan & Jia, Chaoyu & Jia, Hongjie & Mu, Yunfei & Xu, Xiandong & Yu, Xiaodan, 2025. "A planning method for park-level integrated energy system based on system integration theory," Applied Energy, Elsevier, vol. 388(C).
    18. Sana Iqbal & Mohammad Sarfraz & Mohammad Ayyub & Mohd Tariq & Ripon K. Chakrabortty & Michael J. Ryan & Basem Alamri, 2021. "A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment," Sustainability, MDPI, vol. 13(13), pages 1-23, June.
    19. Fei Xue & Zhonghua Gou & Stephen Siu Yu Lau, 2016. "Human Factors in Green Office Building Design: The Impact of Workplace Green Features on Health Perceptions in High-Rise High-Density Asian Cities," Sustainability, MDPI, vol. 8(11), pages 1-16, October.
    20. Barbara Mayer & Michaela Killian & Martin Kozek, 2017. "Hierarchical Model Predictive Control for Sustainable Building Automation," Sustainability, MDPI, vol. 9(2), pages 1-20, February.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:jsusta:v:9:y:2017:i:8:p:1462-:d:108703. 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 The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address (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.