Estimation of Effective Thermal Parameters of Heating Sources Based on Dynamic Measurements in Smart Home

: pp. 58 - 66
Ivan Franko National University of Lviv
Ivan Franko National University of Lviv
Ivan Franko National University of Lviv
Ivan Franko National University of Lviv

This work is devoted to the method of determining the effective thermal parameters of heating sources in a smart home, which involves a combination of algorithms for data analysis and the equation of the physical process of heat transfer. The use of such parameters allows one to create software and hardware solutions for modeling the thermal map of the house, as well as to analyze energy consumption using the machine learning models. Since, for the most part, the total consumption of heating energy is known, it is of interest to determine the part of the energy that corresponds to the individual heating sources. To this end, the article proposes a mathematical model and algorithm for estimating the effective thermal characteristics of heating sources based on the heat transfer equation and data analysis approaches that can be used to obtain information about individual heating sources. The task of determining such parameters is reduced to two stages. At the first stage, using the finite-difference approach to the heat transfer equation, the effective thermal parameter of the heating sources is determined. Further, according to the data of energy consumption and distributions of room temperatures and temperatures on the surface of heating elements, by applying data analysis methods, an algorithm for estimating individual effective thermal characteristics of heating elements installed in rooms is proposed.

  1. Smart Home [Електронний ресурс]. — 2019. — Режим доступу до ресурсу:
  2. A Data Analysis Technique to Estimate the Thermal Characteristics of a House / S.Tabatabaei, W. Van der Ham, M. Klein, J. Treur. // Energies. — 2017. — № 10. — С. 1358.
  3. Reilly A. The impact of thermal mass on building energy consumption / A. Reilly, O. Kinnane. // Applied Energy. — 2017. — № 198. — С. 108–121.
  4. Heating behavior in English homes: An assessment of indirect calculation methods / T.Kane, S. Firth, T. Hassan, V. Dimitriou. // Energy and Buildings. — 2017. — № 148. — С. 89–105.
  5. Energy saving in smart homes based on consumer behavior: A case study / M.Zehnder, H. Wache, H. Witschel, D. Zanatta. // First IEEE International Smart Cities Conference (ISC2-2015), At Guadalajara, Mexico. — 2015. — С. 1–6.
  6. Performance of a Predictive Model for Calculating Ascent Time to a Target Temperature / J.Moon, M. Chung, H. Song, S. Lee. // Energies. — 2016. — № 9. — С. 1090.
  7. Fundamentals of Heat and Mass Transfer. 6th edition. / F.Incropera, D. DeWitt, T. Bergman, A. Lavine. — New York: John Wiley & Sons, 2006. — 1024 с. — (ISBN-13: 978-0471457282).
  8. Sinkevych O., Monastyrskyi L., Sokolovskyi B., Matchyshyn Z. (2019) Klasternyi analiz enerhetychnykh chasovykh riadiv rozumnoho budynku [Cluster analysis of smart home energy time series]. Materialy IV Mizhnarodnoi naukovo-tekhnichnoi konferentsii “Teoretychni ta prykladni aspekty radiotekhniky, pryladobuduvannia I kompʼiuternykh technologic” prysviachena 80-ty richchiu z dnia narodzhennia profesora Ya.I. Protsia (Tern., 20-21 June 2019), pp. 237-240 [in Ukrainian].
  9. Sinkevych O. Statistical Analysis of the Thermal Parameters of Smart Homes / O. Sinkevych, L. Monastyrskii, B. Sokolovskyi. // Electronics and information technologies. — 2018. — № 10. — С. 99–108.
  10. Grus J. Data Science from Scratch. / Joe Grus. — Sebastopol: O’Reilly Media, 2015. — 330 с. — (ISBN-13: 978-1491901427).
  11. Dangeti P. Statistics for Machine Learning / Pratap Dangeti. — Birmingham, UK: Packt Publishing, 2017. — 444 с.
  12. Mukherjee S. F# for Machine Learning Essentials / Sudipta Mukherjee. — Birmingham, UK: Packt Publishing, 2016. — 194 с. — (ISBN-13: 978-1783989348).