Thermal-electrical analogy in dynamic simulations of buildings: comparison of four numerical solution methods

Michalak, Piotr

doi:10.30464/jmee.2020.4.2.179

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Tytuł artykułu

Thermal-electrical analogy in dynamic simulations of buildings: comparison of four numerical solution methods

Autorzy

Michalak Piotr

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DOI

10.30464/jmee.2020.4.2.179

Warianty tytułu

Języki publikacji

Abstrakty

The lumped capacitance method is widely used in dynamic modelling of buildings. Models differ in complexity, solution methods and ability to simulate transient behaviour of described objects. The paper presents a mathematical description of a simple 1R1C thermal network model of a building zone. Four numerical methods were applied to solve differential equation describing its dynamics. For validation purposes two test cases (600 and 900) from the BESTEST procedure were used. In both cases detailed results were given. Better ability of the simulation model to reproduce transient behaviour of the modelled buildings was noticed in case of the lightweight object (case 600). Annual heating and cooling demand was within the reference range for heavyweight one (case 900). The kind of the computation method had no significant effect on simulation results.

Słowa kluczowe

thermal-electrical analogy RC network lumped capacitance BESTEST Euler method Heun's method

analogia termiczno-elektryczna sieć RC pojemność BESTEST metoda Eulera metoda Heuna

Wydawca

Wydawnictwo Uczelniane Politechniki Koszalińskiej

Czasopismo

Journal of Mechanical and Energy Engineering

Rocznik

2020

Tom

Vol. 4, No 2

Strony

179--188

Opis fizyczny

Bibliogr. 70 poz., rys., tab., wykr.

Twórcy

autor

Michalak Piotr

pmichal@agh.edu.pl

Faculty of Mechanical Engineering and Robotics, Department of Power Systems and Environmental Protection Facilities, AGH University of Science and Technology, Mickiewicza 30, 30-059 Kraków, Poland

Bibliografia

1. Sikora M., Siwek K. (2018). Energy audit of the residential building. Journal of Mechanical and Energy Engineering, Vol. 2, No. 4, pp. 317-328 https://doi.org/10.30464/jmee.2018.2.4.317
2. Hunn B.D. (1996). Fundamentals of Building Energy Dynamics. MIT Press, Cambridge
3. P. Tuomaala, K. Saari. (1994). Selection and evaluation of a thermal simulation method for a building simulator. Helsinki University of Technology, Dept of Energy Engineering, Laboratory of Applied Thermodynamics, Report 78, Espoo https://www.aivc.org/sites/default/files/airbase_8219.pdf
4. Kreith F., West R.E. (1997). CRC Handbook of Energy Efficiency, CRC Press, Bosa Roca, United States
5. Ghiaus C. (2013). Causality issue in the heat balance method for calculating the design heating and cooling load. Energy, Vol. 50, pp. 292-301 https://doi.org/10.1016/j.energy.2012.10.024
6. Kramer R., van Schijndel J., Schellen H. (2012). Simplified thermal and hygric building models: A literature review, Frontiers of Architectural Research. Vol. 1, Issue 4, pp. 318-325 https://doi.org/10.1016/j.foar.2012.09.001
7. Weismanová J. (2012). Dynamic thermal models in building simulations. In: 31. Setkání Kateder Mechaniky Tekutin A Termomechaniky, Mikulov. Proceedings. pp. 26-29. http://147.229.133.13/file/358?file=358&lang=1
8. Hudson G., Underwood C.P. (1999). A simple buildingmodelling procedure for Matlab/Simulink. In: Proceedings of Building Simulation '99, Kyoto, Japan. http://www.ibpsa.org/%5Cproceedings%5CBS1999%5CBS99_PA-05.pdf
9. Węglarz A., Narowski P. (2011). The optimal thermal design of residential buildings using energy simulation and fuzzy sets theory. In: Proceedings of Building Simulation 2011: 12th Conference of International Building Performance Simulation Association, Sydney. http://www.ibpsa.org/proceedings/BS2011/P_1277.pdf
10. Wilson M.B., Luck R., Mago P.J. (2015). A First-Order Study of Reduced Energy Consumption via Increased Thermal Capacitance with Thermal Storage Management in a Micro-Building. Energies, Vol. 8, No. 10, pp. 12266-12282. https://doi.org/10.3390/en81012266
11. Oliveira Panão M.J.N., Santos C.A.P., Mateus N.M., Carrilho da Graca G. (2016). Validation of a lumped RC model for thermal simulation of a double skin natural and mechanical ventilated test cell. Energy and Buildings, Vol. 121, pp. 92-103 http://dx.doi.org/10.1016/j.enbuild.2016.03.054
12. Kuniyoshi R., Kramer M., Lindauer M. (2018). Validation of RC Building Models for Applications in Energy and Demand Side Management. In: Proceedings of eSim 2018, the 10ᵗʰ conference of IBPSA-Canada, Montréal, QC, Canada, May 9-10, 2018, pp. 133-142. http://www.ibpsa.org/proceedings/eSimPapers/2018/1-2-B-4.pdf
13. Fuchs M., Teichmann J., Lauster M., Remmen P., Streblow R., Müller D. (2016). Workflow automation for combined modeling of buildings and district energy systems. Energy, Vol. 117, pp. 478-484 http://dx.doi.org/10.1016/j.energy.2016.04.023
14. Vivian J., Mazzi N. (2019). An algorithm for the optimal management of air-source heat pumps and PV systems. Journal of Physics: Conference Series, Vol. 1343, Art. No. 012069. doi:10.1088/1742-6596/1343/1/012069
15. Lauster M., Remmen P., Fuchs M., Teichmann J., Streblow R., Müller D. (2014). Modelling long-wave radiation heat exchange for thermal network building simulations at urban scale using Modelica. In: Proceedings of the 10th International Modelica Conference, March 10-12, 2014, Lund, Sweden, pp. 125-133. doi: 10.3384/ECP14096125
16. PN-EN ISO 13790:2009 (2009): Energy performance of buildings. Calculation of energy use for space heating and cooling. The Polish Committee for Standardization
17. K.J. Kontoleon, D.K. Bikas. (2002). Modeling the influence of glazed openings percentage and type of glazing on the thermal zone behavior. Energy and Buildings, Vol. 34, pp. 389-399 https://doi.org/10.1016/S0378-7788(01)00125-6
18. Lebrun J. (1989). Modelling of Thermal Systems from Technical Sketches to Equations. In: Proceedings of Building Simulation '89, Vancouver, British Columbia, Canada, June 23- 24, 1989, pp. 13-18
19. Desoer Ch.A., Kuh E.S. (1969). Basic Circuit Theory. McGraw-Hill Book Company, New York
20. Wang Y., Liu Y., Wang D., Liu J. (2014). Effect of the night ventilation rate on the indoor environment and air-conditioning load while considering wall inner surface moisture transfer. Energy and Buildings, Vol. 80, pp. 366-374. http://dx.doi.org/10.1016/j.enbuild.2014.05.051
21. Landsman J., Brager G., Doctor-Pingel M. (2018). Performance, prediction, optimization, and user behavior of night ventilation. Energy and Buildings, Vol. 166, pp. 60-72. https://doi.org/10.1016/j.enbuild.2018.01.026
22. Anand P., Sekhar C., Cheong D., Santamouris M., Kondepudi S. (2019). Occupancy-based zone-level VAV system control implications on thermal comfort, ventilation, indoor air quality and building energy efficiency. Energy and Buildings, Vol. 204, Art. no. 109473. https://doi.org/10.1016/j.enbuild.2019.109473
23. Michalak P., Grygierczyk S. (2019). Temperature efficiency of heat exchangers in air handling units. Journal of Mechanical and Energy Engineering, Vol. 3 (43), No. 3, pp. 267-272. DOI: 10.30464/jmee.2019.3.3.267
24. Sonderegger R. (1978). Diagnostic Tests Determining the Thermal Response of a House. Lawrence Berkeley Laboratory. https://escholarship.org/uc/item/4x96z8c5
25. Bruno R., Brombach U., Steinmuller B. (1979). On Calculating Heating and Cooling Requirements. Energy and Buildings, Vol. 2, No. 3, pp. 197-202 https://doi.org/10.1016/0378-7788(79)90004-5
26. Braham W. (1981). Dynamic Indices of Building Thermal Performance. Departmental Papers (Architecture), Vol. 28, pp. 141-145 http://repository.upenn.edu/arch_papers/28
27. Wortman D., O'Doherty B., Judkoff R. (1981). Implementation of an analytical verification technique on three building energy-analysis codes: SUNCAT 2.4, DOE 2.1, and DEROB III, Solar Energy Research Institute. https://www.nrel.gov/docs/legosti/old/1008.pdf
28. Nielsen A., Nielsen B.K. (1984). A Dynamic Test Method for the Thermal Performance of Small Houses: Model Set-up, Test Design, Measurements, Parameter Estimation and Simulation. In: Proceedings of American Council for an Energy-Efficient Economy Symposium, Santa Cruz, California, USA, August 1984. https://www.aceee.org/files/proceedings/1984/data/papers/SS84_Panel2_Paper_17.pdf
29. Subbarao K., Burch J., Hancock E., Jeon H. (1985). Measurement of effective thermal capacitance in buildings. In: Proceedings ASHRAE/DOE/BTECC Conference on Thermal Performance of the Exterior Envelope of Buildings, Clearwater Beach, Florida, USA. https://web.ornl.gov/sci/buildings/conf-archive/1985%20B3%20papers/026.pdf
30. Van Der Maas J., Maldonado E. (1997). A new thermal inertia model based on effusivity. International Journal of Sustainable Energy, Vol. 19, No. 1, pp. 131-160. http://dx.doi.org/10.1080/01425919708914334
31. Sirén K., Hasan A. (2007). Comparison of two calculation methods used to estimate cooling energy demand and indoor summer temperatures. In: Clima 2007 Well Being Indoors, 10-14 June, Finland, Helsinki. https://www.irbnet.de/daten/iconda/CIB7990.pdf
32. Kopecký P. (2011). Two Simplified Thermal Models of the Ventilated Zone. In: Tywoniak, J. (edt.) Sustainable constuction of buildings – Udržitelná výstavba budov. Praha, pp. 39-44
33. Široký J., Oldewurtel F., Cigler J., Prívara S. (2011). Experimental analysis of model predictive control for an energy efficient building heating system. Applied Energy, Vol. 88, pp. 3079-3087 doi:10.1016/j.apenergy.2011.03.009
34. Missaoui R., Joumaa H., Ploix S., Bach S. (2014). Managing energy Smart Homes according to energy prices: Analysis of a Building Energy Management System. Energy and Buildings, Vol. 71, pp. 155-167. http://dx.doi.org/10.1016/j.enbuild.2013.12.018
35. Wilson M.B., Luck R., Mago P.J. (2015). A First-Order Study of Reduced Energy Consumption via Increased Thermal Capacitance with Thermal Storage Management in a Micro-Building. Energies, Vol. 8, pp. 12266-12282. doi:10.3390/en81012266
36. Vivian J., Zarrella A., Emmi G., De Carli M. (2017). An evaluation of the suitability of lumped-capacitance models in calculating energy needs and thermal behaviour of buildings. Energy and Buildings, Vol. 150, pp. 447-465 http://dx.doi.org/10.1016/j.enbuild.2017.06.021
37. Kopecký P., Staněk K. (2014). Estimating the input parameters of lumped building thermal models on the basis of standard design values. In: NSB 2014, 10thNordic Symposium on Building Physics, 15-19 June 2014 Lund, Sweden, Full papers - NSB 2014, pp. 742-749
38. Kopecký P., Staněk K. (2018). Review of Three White-Box Lumped Parameter Building Thermal Models. Heating, Ventilation, Sanitation, Vol. 6/2018, pp. 348-355. http://www.stpcr.cz/en/issue-2018-6
39. Wit de M.H., Driessen H.H., Velden van der R.M.M. (1987). ELAN: a computer model for building energy design: theory and validation. Technische Universiteit Eindhoven
40. Ashouri A., Fazlollahi S., Benz M.J., Maréchal F. (2015). Particle Swarm Optimization and Kalman Filtering for Demand Prediction of Commercial Buildings. In: Proceedings of ECOS 2015 - The 28th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, June 30-July 3, 2015, Pau, France
41. Park H., Ruellan M., Bouvet A., Monmasson E., Bennacer R. (2011). Thermal parameter identification of simplified building model with electric appliance. In: Proceedings of the 11th international conference on electrical power quality and utilisation (EPQU). Vol. no. 17-19. DOI: 10.1109/EPQU.2011.6128822
42. Ramallo-González A.P., Brown M., Coley D.A. (2015).Identifying the ideal topology of simple models to represent dwellings. In: Proceedings of BS2015: 14th Conference of International Building Performance Simulation Association, Hyderabad, India, Dec. 7-9, 2015, pp. 688-695 http://www.ibpsa.org/proceedings/BS2015/p2432.pdf
43. Harb H., Boyanov N., Hernandez L., Streblow R., Müller D. (2016). Development and validation of grey-box models for forecasting the thermal response of occupied buildings. Energy and Buildings, Vol. 117, pp. 199-207. http://dx.doi.org/10.1016/j.enbuild.2016.02.021
44. PN-EN 28601:2002 (2002): Data elements and interchange formats - Information interchange - Representation of dates and times. The Polish Committee for Standardization
45. Narowski P., Janicki M., Heim D. (2013). Comparison of untypical meteorological years (UMY) and their influence on building energy performance simulations. In: Proceedings of BS2013: 13th Conference of International Building Performance Simulation Association, Chambéry, France, August 26-28. http://www.ibpsa.org/proceedings/BS2013/p_1466.pdf
46. Kulesza K. (2017). Comparison of typical meteorological year and multi-year time series of solar conditions for Belsk, central Poland. Renewable Energy, Vol. 113, pp. 1135-1140 https://doi.org/10.1016/j.renene.2017.06.087
47. Rudniak J. (2019). Solar parameters of the local climate during the summer in relation to data from typical meteorological year. E3S Web Conf. Vol. 116 Art. no. 00066. https://doi.org/10.1051/e3sconf/201911600066
48. Loutzenhiser P., Manz H., Maxwell G. (2007). Empirical Validations of Shading/Daylighting/Load Interactions in Building Energy Simulation Tools. A Report for the International Energy Agency’s SHC Task 34. http://www.equaonline.com/iceuser/validation/IEATask34.pdf
49. Zhu D., Yan D., Wang C, Hong T. (2012). Comparison of Building Energy Modeling Programs: Building Loads. LBNL Report E, vol. 6034 https://www.osti.gov/ servlets/purl/1168735
50. Venkateshan S.P., Swaminathan P. (2014). Computational Methods in Engineering. Academic Press https://doi.org/10.1016/B978-0-12-416702-5.50008-9
51. Jayanti S. (2018). Computational Fluid Dynamics for Engineers and Scientists. Springer, Dordrecht. https://doi.org/10.1007/978-94-024-1217-8
52. Curnier A. (1994). Computational Methods in Solid Mechanics. Springer Science+Business Media, Dordrecht https://doi.org/10.1007/978-94-011-1112-6
53. Hundsdorfer W., Verwer J. (2003). Numerical Solution of Time-Dependent Advection-Diffusion-Reaction Equations. Springer-Verlag Berlin Heidelberg https://doi.org/10.1007/978-3-662-09017-6
54. Ooi A. (2005). Advanced Computational Mechanics. Lecture notes. The University of Melbourne. https://people.eng.unimelb.edu.au/asho/AdvCompMech/notes.pdf
55. Vande Wouwer A., Saucez P., Vilas C. (2014). Simulation of ODE/PDE Models with MATLAB®, OCTAVE and SCILAB. Springer International Publishing, Cham https://doi.org/10.1007/978-3-319-06790-2
56. Skre Fjordholm U. (2018). Numerical methods for ordinary differential equations. Lecture notes. University of Oslo https://www.uio.no/studier/emner/matnat/math/MAT3440/v18/pensumliste/numerical_methods.pdf
57. Neymark J., Judkoff R., Knabe G., Le H.T., Dürig M., Glass A., Zweifel G. (2002). Applying the building energy simulation test (BESTEST) diagnostic method to verification of space conditioning equipment models used in whole-building energy simulation programs. Energy and Buildings, Vol. 34, No. 9, pp. 917-93, https://doi.org/10.1016/S0378-7788(02)00072-5
58. ANSI/ASHRAE, Standard 140-2017 (2017): Standard Method of Test for the Evaluation of Building Energy Analysis Computer Programs. American National Standards Institute
59. Narowski P., Mijakowski M., Panek A., Rucińska J., Sowa J. (2010). Proposal of simplified calculation 6R1C method of buildings energy performance adopted to Polish conditions. In: Central Europe towards Sustainable Building Energy Efficiency, CESB10 Prague. http://www.irbnet.de/daten/iconda/CIB17831.pdf
60. Narowski P., Mijakowski M., Panek A., Rucińska J., Sowa J. (2010). Integrated 6R1C energy simulation method - principles, verification and application. In: CLIMA 2010, 11th REHVA World Congress “Sustainable Energy Use in Buildings”, Antalya, Turkey
61. Gajewski R., Pieniążek P. (2017). Building energy modelling and simulations: qualitative and quantitative analysis. In: MATEC Web Conf., Vol. 117, Art. no. 00051 DOI: 10.1051/matecconf/201711700051
62. Gajewski R.R., Kułakowski T. (2018). Towards Optimal Design of Energy Efficient Buildings. Archives of Civil Engineering, Vol. 64, No. 4, pp. 135-153. https://doi.org/10.2478/ace-2018-0067
63. Henninger R.H., Witte M.J. (2004). Energy Plus Testing with ANSI/ASHRAE Standard 140-2001 (BESTEST) Energy Plus Version 1.2.0.029. Ernest Orlando Lawrence Berkeley National Laboratory Berkeley, California. https://simulationresearch.lbl.gov/dirpubs/epl_bestest_ash.pdf
64. PN-EN ISO 13786:2007 (2007): Thermal performance of building components - Dynamic thermal characteristics - Calculation methods. The Polish Committee for Standardization
65. Michalak P. (2019). Modelling of global solar irradiance on sloped surfaces in climatic conditions of Kraków. New Trends in Production Engineering, Vol. 2, No. 1, pp. 505-514 doi: https://doi.org/10.2478/ntpe-2019-0054
66. Jędrzejuk H., Rucińska J. (2015). Verifying a need of artificial cooling - a simplified method dedicated to single-family houses in Poland. Energy Procedia, Vol. 78, pp. 1093-1098. doi: 10.1016/j.egypro.2015.11.061
67. Capizzi G., Lo Sciuto G., Cammarata G., Cammarata M. (2017). Thermal transients simulations of a building by a dynamic model based on thermal-electrical analogy: Evaluation and implementation issue. Applied Energy, Vol. 199, pp. 323-334 http://dx.doi.org/10.1016/j.apenergy.2017.05.052
68. Costantino A., Fabrizio E., Ghiggini A., Bariani M.(2018). Climate control in broiler houses: A thermal model for the calculation of the energy use and indoor environmental conditions. Energy and Buildings, Vol. 169, pp. 110-126 https://doi.org/10.1016/j.enbuild.2018.03.056
69. Elci M., Delgado B.M., Henning H.M., Henze G.P., Herkel S. (2018). Aggregation of residential buildings for thermal building simulations on an urban district scale. Sustainable Cities and Societies, Vol. 39, pp. 537-547 https://doi.org/10.1016/j.scs.2018.03.015
70. Horvat I., Dović D. (2016). Dynamic modeling approach for determining buildings technical system energy performance, Energy Conversion and Management, Vol. 125, pp. 154-165. http://dx.doi.org/10.1016/j.enconman.2016.03.062

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Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).

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