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

S. Tabatabaei, Wim van der Ham, Michel C. A. Klein, Jan Treur

Research output: Contribution to JournalArticleAcademicpeer-review

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.
LanguageEnglish
Article number1358
JournalEnergies
Volume10
Issue number9
DOIs
StatePublished - 8 Sep 2017

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Data analysis
Thermostats
Thermostat
Estimate
Space heating
Heat Capacity
Specific heat
Insulation
Energy Saving
Energy conservation
Energy
Energy utilization
Energy Consumption
Heating
Sector
Hot Temperature
Gases
Experiments
Experiment
Temperature

Cite this

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title = "A Data Analysis Technique to Estimate the Thermal Characteristics of a House",
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.",
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A Data Analysis Technique to Estimate the Thermal Characteristics of a House. / Tabatabaei, S.; van der Ham, Wim; Klein, Michel C. A.; Treur, Jan.

In: Energies, Vol. 10, No. 9, 1358, 08.09.2017.

Research output: Contribution to JournalArticleAcademicpeer-review

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AB - 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.

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