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Estimation of Building Thermal Performance using Simple Sensors and Air Conditioners

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  • Yuiko Sakuma

    (Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku, Yokohama, Kanagawa 223-8522, Japan)

  • Hiroaki Nishi

    (Department of System Design Engineering, Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku, Yokohama, Kanagawa 223-8522, Japan)

Abstract

Energy and environmental problems have attracted attention worldwide. Energy consumption in residential sectors accounts for a large percentage of total consumption. Several retrofit schemes, which insulate building envelopes to increase energy efficiency, have been adapted to address residential energy problems. However, these schemes often fail to balance the installment cost with savings from the retrofits. To maximize the benefit, selecting houses with low thermal performance by a cost-effective method is inevitable. Therefore, an accurate, low-cost, and undemanding housing assessment method is required. This paper proposes a thermal performance assessment method for residential housing. The proposed method enables assessments under the existing conditions of residential housings and only requires a simple and affordable monitoring system of power meters for an air conditioner (AC), simple sensors (three thermometers at most), a BLE beacon, and smartphone application. The proposed method is evaluated thoroughly by using both simulation and experimental data. Analysis of estimation errors is also conducted. Our method shows that the accuracy achieved with the proposed three-room model is 9.8% (relative error) for the simulation data. Assessments on the experimental data also show that our proposed method achieved Ua value estimations using a low-cost system, satisfying the requirements of housing assessments for retrofits.

Suggested Citation

  • Yuiko Sakuma & Hiroaki Nishi, 2019. "Estimation of Building Thermal Performance using Simple Sensors and Air Conditioners," Energies, MDPI, vol. 12(15), pages 1-22, July.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:15:p:2950-:d:253581
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    References listed on IDEAS

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    1. Francesco Asdrubali & Cinzia Buratti & Franco Cotana & Giorgio Baldinelli & Michele Goretti & Elisa Moretti & Catia Baldassarri & Elisa Belloni & Francesco Bianchi & Antonella Rotili & Marco Vergoni &, 2013. "Evaluation of Green Buildings’ Overall Performance through in Situ Monitoring and Simulations," Energies, MDPI, vol. 6(12), pages 1-23, December.
    2. Schiavoni, S. & D׳Alessandro, F. & Bianchi, F. & Asdrubali, F., 2016. "Insulation materials for the building sector: A review and comparative analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 988-1011.
    3. Virginia Gori & Phillip Biddulph & Clifford A. Elwell, 2018. "Correction: Gori, V.; Biddulph, P.; Elwell, C.A. A Bayesian Dynamic Method to Estimate the Thermophysical Properties of Building Elements in All Seasons, Orientations and with Reduced Error. Energies ," Energies, MDPI, vol. 11(9), pages 1-1, September.
    4. Virginia Gori & Phillip Biddulph & Clifford A. Elwell, 2018. "A Bayesian Dynamic Method to Estimate the Thermophysical Properties of Building Elements in All Seasons, Orientations and with Reduced Error," Energies, MDPI, vol. 11(4), pages 1-27, March.
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    Cited by:

    1. Isidro Calvo & Aitana Espin & Jose Miguel Gil-García & Pablo Fernández Bustamante & Oscar Barambones & Estibaliz Apiñaniz, 2022. "Scalable IoT Architecture for Monitoring IEQ Conditions in Public and Private Buildings," Energies, MDPI, vol. 15(6), pages 1-23, March.
    2. Carlos Ramos & Zita Vale & Peter Palensky & Hiroaki Nishi, 2021. "Sustainable Energy Consumption," Energies, MDPI, vol. 14(20), pages 1-3, October.

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