Automated in-situ determination of buildings’ global thermo-physical characteristics and air change rates through inverse modelling of smart meter and air temperature data

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Abstract

The advancement of smart metering and sensor technologies has opened the door to performing extensive in-situ measurements in buildings and a tendency to carry-out detailed energy and indoor climate monitoring, leading to the availability of the so-called “on-board monitoring data”. The data obtained through these measurements is of high value as it can be used for identification of parameters determining health, thermal comfort, and energy use. In this article, an occupied dwelling has been inspected and monitored for one year and the in-situ measurement and meteorological data are combined to feed a physic-based energy model. For the first time, the detailed data cleaning and filtering techniques are explained to give insight for future similar studies. The data is fed to a 1st – order circuit RC model, equivalent to the building's thermal model. Next, using Genetic Algorithm in a stated optimization problem, Inverse Modelling has been applied to identify four main global thermo-physical characteristics of the building, with a special attention to the heat loss coefficient. The results are compared by analysing three feed data properties: granularity level, period length, and time period, resulting the best fit in the coldest periods. The outcomes have shown the importance of these data properties by revealing differences in the heat loss coefficient in different periods and the weakening of the heat capacitance effect when feeding the model with low granularity level data. The daily values of the heat loss coefficient are then applied in combination with construction data to determine the daily averages of hourly air change rates. Finally, the method has been evaluated in terms of accuracy and precision and the air change rates have been validated using CO2 concentration and wind velocity. Using this method, it is possible to determine buildings’ main global thermo-physical characteristics as well as the cold periods’ airborne heat losses.

Original languageEnglish
Article number110484
Number of pages18
JournalEnergy and Buildings
Volume229
DOIs
Publication statusPublished - 2020

Keywords

  • Air change rate
  • Heat loss coefficient
  • Inverse modelling
  • Smart meter

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