Possibilities of regression analysis in processing thermal conductivity measurement data

• Autorzy: Hotra Oleksandra , Grądz Żaklin , Kovtun Svitlana , Boyko Oksana , Hanus Robert , Wilk Barbara

Identyfikatory

DOI

10.12913/22998624/200381

• Języki publikacji: EN

Abstrakty

EN

When implementing energy saving measures, the key is the correct choice of thermal insulation materials, the main characteristic of which is the thermal conductivity coefficient. Missing part of the data, which may occur during investigation of materials in natural conditions, can lead to incorrect determination of the corresponding characteristic, which negatively affects the effectiveness of the implemented measures and energy saving. Therefore, reconstruction of the missing data at the stage of preliminary processing of measured signals to obtain complete and accurate data when determining the thermal conductivity of thermal insulation materials will avoid this situation. The article presents the results of regression analysis of data obtained during express control of thermal conductivity of thermal insulation materials based on the local thermal impact method. Regression models were built for signal reconstruction with 10%, 20% and 30% missing data, using which a relative error of determination the thermal conductivity coefficient of less than 8% was obtained. This is acceptable for express control of thermal conductivity and indicates the correctness of data restoration in this way. In addition, an algorithm is provided for determining signal stationarity, which allows to reasonably reduce the duration of each material with a given level of permissible error.

Słowa kluczowe

EN

thermal conductivity determination; insulation materials; regression analysis; missing data; data processing

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2025
• Tom: Vol. 19, no. 4
• Strony: 294–303
• Opis fizyczny: Bibliogr. 26 poz., fig., tab.

Twórcy

autor

Hotra Oleksandra

• Department of Electronics and Information Technology, Lublin University of Technology, ul. Nadbystrzycka 38D, 20-618 Lublin, Poland

• https://orcid.org/0000-0003-2074-347X

autor

Grądz Żaklin

• Department of Electronics and Information Technology, Lublin University of Technology, ul. Nadbystrzycka 38D, 20-618 Lublin, Poland

• https://orcid.org/0000-0003-1902-4953

autor

Kovtun Svitlana

• Department of Monitoring and Diagnostic of Energy Objects, General Energy Institute of NAS of Ukraine, Antonovich St. 172, 03057 Kyiv, Ukraine

• https://orcid.org/0000-0002-6596-3460

autor

Boyko Oksana

• Medical Informatics Department, Danylo Halytsky Lviv National Medical University, 69 Pekarska Str., 79010 Lviv, Ukraine

• https://orcid.org/0000-0002-8810-8969

autor

Hanus Robert

• Department of Metrology and Diagnostic Systems, Rzeszów University of Technology, al. Powstańców Warszawy 12, 35-959 Rzeszów, Poland

• https://orcid.org/0000-0001-8595-1478

autor

Wilk Barbara

• Department of Metrology and Diagnostic Systems, Rzeszów University of Technology, al. Powstańców Warszawy 12, 35-959 Rzeszów, Poland

Bibliografia

• 1. Ricciu R., Besalduch L. A., Galatioto A., Ciulla G. Thermal characterization of insulating materials. Renewable and Sustainable Energy Reviews 2018; 82, Part 2: 1765–1773. https://doi.org/10.1016/j.rser.2017.06.057.
• 2. Schiavoni S., D׳Alessandro F., Bianchi F., Asdrubali F. Insulation materials for the building sector: A review and comparative analysis. Renewable and Sustainable Energy Reviews 2016; 62: 988–1011. https://doi.org/10.1016/j.rser.2016.05.045.
• 3. Zhang H., Shang C., Tang G. Measurement and identification of temperature-dependent thermal conductivity for thermal insulation materials under large temperature difference. International Journal of Thermal Sciences 2022; 171: 107261. https://doi.org/10.1016/j.ijthermalsci.2021.107261.
• 4. Berardi U., Naldi M. The impact of the temperature dependent thermal conductivity of insulating materials on the effective building envelope performance. Energy and Buildings 2017; 144: 262–275. https://doi.org/10.1016/j.enbuild.2017.03.052.
• 5. Domínguez-Muñoz F., Anderson B., Cejudo-López J. M., Carrillo-Andrés A. Uncertainty in the thermal conductivity of insulation materials. Energy and Buildings, 2010; 42(11): 2159–2168. https://doi.org/10.1016/j.enbuild.2010.07.006.
• 6. Wu X., Niu J., Tian Z., Li X. An evidence theory-based uncertainty quantification method of building thermal parameters for accurate reliability assessment of building air-conditioning design loads. Energy and Buildings 2024; 319: 114424. https://doi.org/10.1016/j.enbuild.2024.114424.
• 7. Deng S., Cen J., Song H., Xiong J., Chen Z. A hybrid predictive model with an error-trigger adjusting method of thermal load in super-high buildings. Energy and Buildings 2025; 328: 115081. https://doi.org/10.1016/j.enbuild.2024.115081.
• 8. Kishore R. A., Bianchi M. V. A., Booten C., Vidal J., Jackson R. Parametric and sensitivity analysis of a PCM-integrated wall for optimal thermal load modulation in lightweight buildings. Applied Thermal Engineering 2021; 187: 116568. https://doi.org/10.1016/j.applthermaleng.2021.116568.
• 9. Baldinelli G., Bianchi F., Gendelis S, et al. Thermal conductivity measurement of insulating innovative building materials by hot plate and heat flow meter devices: A Round Robin Test. International Journal of Thermal Sciences 2019; 139: 25–35. https://doi.org/10.1016/j.ijthermalsci.2019.01.037.
• 10. ISO 8301, Thermal Insulation – Determination of Steady-State Thermal Resistance and Related Properties – Heat Flow Meter Apparatus, ISO, Geneva, Switzerland, 1991.
• 11. ISO 8302, Thermal insulation – Determination of steady-state thermal resistance and related properties – Guarded hot plate apparatus, ISO, Geneva, Switzerland, 1991.
• 12. Pásztory Z., Anh Le D.H. An overview of factors influencing thermal conductivity of building insulation materials. J. Build. Eng. 2021; 102604: 1–16. https://doi.org/10.1016/j.jobe.2021.102604.
• 13. Zhang J., Huang M., Wan N., Deng Zh., He Zh., Luo J. Missing measurement data recovery methods in structural health monitoring: The state, challenges and case study. Measurement 2024; 231: 114528. https://doi.org/10.1016/j.measurement.2024.114528.
• 14. Sefidian A.M., Daneshpour N. Estimating missing data using novel correlation maximization based methods. Applied Soft Computing 2020; 91: 106249. https://doi.org/10.1016/j.asoc.2020.106249.
• 15. Hotra O., Kovtun S., Dekusha O., Grądz Ż. Prospects for the application of wavelet analysis to the results of thermal conductivity express control of thermal insulation materials. Energies 2021; 14(17): 5223. https://doi.org/10.3390/en14175223.
• 16. Hotra O., Dekusha O. A device for thermal conductivity measurement based on the method of local heat influence. Przegląd Elektrotechniczny 2012; 88(5A): 223–226. http://pe.org.pl/article-s/2012/5a/55.pdf (Accessed: 10.12.2024).
• 17. Dekusha L., Kovtun S., Dekusha O. Heat Flux Control in Non-stationary Conditions for Industry Applications, In: Proceedings of the 2019 IEEE 2nd Ukraine Conference on Electrical and Computer Engineering (UKRCON), Lviv, Ukraine, 2019; 601–605. https://ieeexplore.ieee.org/document/8879847 (Accessed: 10.12.2024).
• 18. Eze C.M., Asogwa O.C., Onwuamaeze U.C. Regression analysis of an experiment with treatment as a qualitative predictor regression analysis of an experiment with treatment as a qualitative predictor. Global Journal of Science Frontier Research: (F) Mathematics and Decision Sciences 2018; 18(4): 14–27. https://globaljournals.org/GJSFR_Volume18/3-Regression-Analysis-of-an-Experiment.pdf.
• 19. Prahutama, A., Utami, T.W. Modelling Fourier regression for time series data-a case study: modelling inflation in foods sector in Indonesia. In: Journal of Physics: Conference Series, IOP Publishing; 2018; 974(1): 012067. http://dx.doi.org/10.1088/1742-6596/974/1/012067.
• 20. Santoso R., Prahutama A., Devi, A.R. Modeling longitudinal data based on Fourier regression. In Journal of Physics: Conference Series, IOP Publishing.; 2019; 1217(1): 012105. http://dx.doi.org/10.1088/1742-6596/1217/1/012105.
• 21. Taiwo A., Olatayo T. An improved procedure for Fourier regression analysis. Anale. Seria Informatică 2018; 16: 108–112. https://anale-informatica.tibiscus.ro/download/lucrari/16-2-16-Taiwo.pdf (Accessed: 10.12.2024).
• 22. Esaki T. Appropriate evaluation measurements for regression models. Chem-Bio Informatics Journal 2021; 21: 59–69. https://doi.org/10.1273/cbij.21.59.
• 23. Chai T., Draxler R. R. Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geosci. Model Dev. 2014; 7: 1247–1250. http://dx.doi.org/10.5194/gmd-7-1247-2014.
• 24. Marcelino C. G., Leite G. M., Celes P., Pedreira C. E. Missing data analysis in regression. Applied Artificial Intelligence 2022; 36(1): 2032925. https://www.tandfonline.com/doi/full/10.1080/08839514.2022.2032925 (Accessed: 10.12.2024).
• 25. Schadler D., Stadlober E. Fault detection using online selected data and updated regression models. Measurement 2019; 140: 437–449. https://doi.org/10.1016/j.measurement.2019.04.010.
• 26. Steenbergen M.R. Regression Analysis 2016; Zurich, Switzerland. https://www.suz.uzh.ch/dataforstat/statistik2/inlinks/SRM.pdf (Accessed: 10.12.2024).

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).