A Building energy simulation methodology to validate energy balance and comfort in zero energy buildings

Abstract

The construction of Net Zero Energy Buildings (NZEB) is one of the objectives in the road to the low-carbon economy by 2050. NZEB design includes a reduction of current energy demands and the balance between consumption and on-site energy generation without compromising indoor comfort conditions. Building designers are using building information modeling (BIM) and building energy simulation (BES) tools to validate design decisions and to evaluate energy balance in buildings. However, the flow of information between BIM software and BES tools has not been solved yet. This work proposes a method to address the decision-making process at three different stages of the building design. Initially, the use of BIM over the architectural design process helps architects to make meaningful decisions related to the passive solar heat gains and envelop materials. Secondly, a more advanced BES is used to analyze the strategies of ventilation and the influence of heating ventilation and air conditioning (HVAC) systems. Finally, a new method to integrate water flow glazing (WFG) is implemented to increase the comfort in those areas of the building with a large area of glass. Applying the right strategy for natural ventilation can reduce the thermal loads by 45% in Summer. Using WFG minimizes the gap between indoor air temperature and operative temperature according to the results.

Keywords

• Building energy simulation,Water flow glazing,Zero energy building

Supporting Institution

KEENE STATE COLLEGE

Project Number

FACULTY DEVELOPMENT GRANT

Thanks

Horizon 2020 program. "Indewag" project Ref. 680441

References

1. [1] Jin-Up K, Oussama A, Hadadi, Hyunjoo K, Jonghyeob K. Development of A BIM-Based Maintenance Decision-Making Framework for the Optimization between Energy Efficiency and Investment Costs. Sustainability 2018; 10(7):2480 DOI: 10.3390/su10072480.
2. [2] Harish, V, Kumar, A. Reduced order modeling and parameter identification of a building energy system model through an optimization routine. Applied Energy 2016; 162, 1010-1023, DOI:10.1016/j.apenergy.2015.10.137
3. [3] Reddy A. Applied data analysis and modeling for energy engineers and scientists. New York: Springer; 2011.
4. [4] Sieminski, A. International energy outlook. In Proceedings of the Deloitte Oil and Gas Conference, Houston, TX, USA, 18 November 2014; Energy Information Administration (EIA): Washington, DC, USA, 2014.
5. [5] Carpino, C, Mora, D, de Simone, M. On the use of questionnaire in residential buildings. A review of collected data methodologies and objectives. Energy and Buildings 2019; 186: 297–318 DOI: 10.1016/j.enbuild.2018.12.021
6. [6] Ascione, F, De Masi, R, de Rossi F, Ruggiero S, Vanoli G. Optimization of building envelope design for nZEBs in Mediterranean climate: performance analysis of residential case study. Applied Energy 2016; 183: 938-957 DOI: 10.1016/j.apenergy.2016.09.027
7. [7] Song, K, Kwon, N, Anderson, K, Park, M, Lee, H. Predicting hourly energy consumption in buildings using occupancy-related characteristics of end-user groups. Energy and Buildings 2017; 156: 121-133. DOI: 10.1016/j.enbuild.2017.09.060
8. [8] Wang, Z, Xue, Q, Ji, Y, Yu, Z. Indoor environment quality in a low-energy residential building in winter in Harbin. Building Environment 2018; 135: 194-201. DOI: 10.1016/j.buildenv.2018.03.012

9. [9] Jose Maria Santos-Herrero, Jose Manuel Lopez-Guede, Ivan Flores. A Short review on the use of renewable energies and model predictive control in buildings. Journal of Energy Systems 2017; 1(3): 112-119 DOI: 10.30521/jes.346653
10. [10] Pavan Kumar, Y, Bhimasingu, R. Renewable energy based microgrid system sizing and energy management for green buildings. J. Mod. Power Syst. Clean Energy 2015; 3(1):1–13 DOI: 10.1007/s40565-015-0101-7
11. [11] Allen, S.R, Hammond, G.P, McKenna, R.C. The thermodynamic implications of electricity end-use for heat and power. Proc IMechE Part A: J Power and Energy 2017; 231 (6): 508–525 DOI: 10.1177/0957650917693483
12. [12] Susorova, I, Tabibzadeh, M, Rahman, A, Clack, H.L, Elnimeiri, M. The effect of geometry factors on fenestration energy performance and energy savings in office buildings. Energy and Buildings 2013; 57: 6-13 DOI: 10.1016/j.enbuild.2012.10.035
13. [13] Gasparella, A, Pernigotto, G, Cappelletti, F, Romagnoni, P, Baggio P. Analysis and modelling of window and glazing systems energy performance for a well-insulated residential building. Energy and Buildings 2011; 43(4): 1030-1037, DOI: 10.1016/j.enbuild.2010.12.032
14. [14] Fang, Y, Hyde, T.J, Hewitt, N. Predicted thermal performance of triple vacuum glazing. Solar Energy 2010; 84 (12): 2132-2139. DOI: 10.1016/j.solener.2010.09.002
15. [15] Jelle B.P, Hynd A, Gustavsen A, Arasteh D, Goudey H, Hart R. Fenestration of today and tomorrow: A state-of-the-art review and future research opportunities. Solar Energy Materials and Solar Cells 2012; 96: 1-28 DOI: 10.1016/j.solmat.2011.08.010
16. [16] Amaral, R, Rodrigues, E, Gaspar, AR, Gomes, A. A thermal performance parametric study of window type, orientation, size and shadowing effect. Sustainable Cities and Society 2016; 26: 456-465 DOI: 10.1016/j.scs.2016.05.014
17. [17] Chow T.T, Li C, Lin Z, Thermal characteristics of water-flow double-pane window. International Journal of Thermal Sciences 2010; 50: 140–148, DOI: 10.1016/j.ijthermalsci.2010.10.006
18. [18] Hermanns M, del Ama F, Hernández J.A. Analytical solution to the one-dimensional non-uniform absorption of solar radiation in uncoated and coated single glass panes. Energy and Buildings 2012; 47: 561–571 DOI: 10.1016/j.enbuild.2011.12.034
19. [19] Romero, X, Hernandez, J.A, Spectral problem for water flow glazings. Energy and Buildings 2017; 145: 67-78. DOI: 10.1016/j.enbuild.2017.03.013
20. [20] Harish, V, Kumar, A. A review on modeling and simulation of building energy systems. Renewable and Sustainable Energy Reviews 2016; 56: 1272–1292 DOI: 10.1016/j.rser.2015.12.040
21. [21] Gökgür, A. Current and Future Use of BIM in Renovation Projects. Master’s Thesis, Chalmers University of Technology, Gothenburg, Sweden, 2015.
22. [22] Mascio, D.D, Wang, X. Building Information Modelling (BIM)-Supported Cooperative Design in Sustainable Renovation Projects. In: Proceedings of the International Conference on Cooperative Design, Visualization and Engineering, Alcudia, Spain, 22 September 2013. Ed. Springer: Berlin/Heidelberg, Germany.
23. [23] Volk, R, Stengel, J, Schultmann, F. Building information modelling (BIM) for existing buildings–literature review and future needs. Automation in Construction 2014; 38: 109–127. DOI: 10.1016/j.autcon.2013.10.023
24. [24] Gendebien, S, Georges, E, Bertagnolio, S, Lemort, V., Methodology to characterize a residential building stock using a bottom-up approach: a case study applied to Belgium. International Journal of Sustainable Energy Planning and Management 2014; 04: 71–88. DOI: 10.5278/ijsepm.2014.4.7
25. [25] Kim, H, Kyle, A. Energy modeling system using building information modeling open standards. Journal of Computing in Civil Engineering 2013; 27(3): 203-211. DOI: 10.1061/(ASCE)CP.1943-5487.0000215
26. [26] Catalina, T, Virgone, J, Blanco, E. Development and validation of regression models to predict monthly heating demand for residential building. Energy and Buildings 2008; 40(10): 1825–1832, DOI: 10.1016/j.enbuild.2008.04.001
27. [27] Ekici, B, Aksoy, U. T. Prediction of building energy consumption by using artificial neural network. Advances in Engineering Software 2008; 40(5): 356–362. DOI: 10.1016/j.advengsoft.2008.05.003
28. [28] Olofsson, T, Andersson, S, Sjögren, J. Building energy parameter investigations based on multivariate analysis. Energy and Buildings 2009; 41(1): 71–80. DOI: 10.1016/j.enbuild.2008.07.012
29. [29] Ferrandiz, J, del Ama Gonzalo, F, Sanchez-Sepulveda, M, Fonseca, D. Introducing a new ICT tool in an active learning environment course: Performance consequences depending on the introduction design. International Journal of Engineering Education 2019; 35: 360–371
30. [30] Maljkovic, D. Modelling influential factors of consumption in buildings connected to district heating systems. Energies 2019; 12(4): 586. DOI: 10.3390/en12040586
31. [31] Gorgolis, G, Karamanis, D. Solar energy materials for glazing technologies. Solar Energy Materials and Solar Cells 2016; 144: 559-578. DOI: 10.1016/j.solmat.2015.09.040
32. [32] Ramesh T, Prakash, R, Shukla, KK. Life cycle energy analysis of buildings: an overview. Energy and Buildings 2010 42(10) 1592-1600.
33. [33] Kapsalaki, M, Leal. Santamouris M. A methodology for economic efficient design of Net Zero Energy Buildings. Energy and Buildings 2012, 55: 765-778.
34. [34] de Dear, R, Brager, G. Thermal comfort in naturally ventilated buildings: revisions to ASHRAE Standard 55 Energy and Buildings 2002; 34: 549–561.
35. [35] Marszala, A.J, Heiselberg P, Bourrelle, J.S, Musall, E, Vossc, K, Sartori, I, Napolitano, A. Zero Energy Building – A review of definitions and calculation methodologies. Energy and Buildings 2011; 43: 971–979.
36. [36] Del Ama Gonzalo, F, Hernandez, J.A. Testing of water flow glazing in shallow geothermal systems. Procedia Engineering 2016; 161: 887–891. DOI: 10.1016/j.proeng.2016.08.742
37. [37] Chow, T.T, Li, C, Lin, Z. Innovative solar windows for cooling-demand climate. Solar Energy Materials and Solar Cells 2010; 94(2): 212–220. DOI: 10.1016/j.solmat.2009.09.004
38. [38] Del Ama Gonzalo, F, Moreno, B, Hernandez, J.A. Dynamic Solar Energy Transmittance for Water Flow Glazing in Residential Buildings. International Journal of Applied Engineering Research 2018; 13(11): 9188-9193
39. [39] Karlsson, H. Modelling of Long Wave Radiation Exchange in Enclosures with Building Integrated Heating, Chalmers University of Technology. Dep. of Civil and Environmental Engineering, Sweden, 2005.
40. [40] Fanger, P.O. Thermal Comfort: analysis and applications in environmental engineering. Danish Technical Press, Copenhagen, Denmark, 1970
41. [41] ASHRAE Research Project 657. Simplified Method to factor Mean Radiant Temperature (MRT) into Building and HVAC System Design
42. [42] EN-15251 Indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality, thermal environment, lighting and acoustics.