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A Data Analysis Technique to Estimate the Thermal Characteristics of a House

Author

Listed:
  • Seyed Amin Tabatabaei

    (Behavioural Informatics Group, Deptartment of Computer Science, VU University Amsterdam, 1081 HV Amsterdam, The Netherlands)

  • Wim Van der Ham

    (Quby, Joan Muyskenweg 22, 1096 CJ Amsterdam, The Netherlands)

  • Michel C. A. Klein

    (Behavioural Informatics Group, Deptartment of Computer Science, VU University Amsterdam, 1081 HV Amsterdam, The Netherlands)

  • Jan Treur

    (Behavioural Informatics Group, Deptartment of Computer Science, VU University Amsterdam, 1081 HV Amsterdam, The Netherlands)

Abstract

Almost one third of the energy is used in the residential sector, and space heating is the largest part of energy consumption in our houses. Knowledge about the thermal characteristics of a house can increase the awareness of homeowners about the options to save energy, for example by showing that there is room for improvement of the insulation level. However, calculating the exact value of these characteristics is not possible without precise thermal experiments. In this paper, we propose a method to automatically estimate two of the most important thermal characteristics of a house, i.e., the loss rate and the heat capacity, based on collected data about the temperature and gas usage. The method is evaluated with a data set that has been collected in a real-life case study. Although a ground truth is lacking, the analyses show that there is evidence that this method could provide a feasible way to estimate those values from the thermostat data. More detailed data about the houses in which the data was collected is required to draw stronger conclusions. We conclude that the proposed method is a promising way to add energy saving advice to smart thermostats.

Suggested Citation

  • Seyed Amin Tabatabaei & Wim Van der Ham & Michel C. A. Klein & Jan Treur, 2017. "A Data Analysis Technique to Estimate the Thermal Characteristics of a House," Energies, MDPI, vol. 10(9), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:9:p:1358-:d:111320
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    References listed on IDEAS

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    Cited by:

    1. Jenny Crawley & Despina Manouseli & Peter Mallaburn & Cliff Elwell, 2022. "An Empirical Energy Demand Flexibility Metric for Residential Properties," Energies, MDPI, vol. 15(14), pages 1-18, July.

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