AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (2.3 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article | Open Access

Stochastic energy-demand analyses with random input parameters for the single-family house

Marcin KoniorczykWitold Grymin( )Marcin ZygmuntDalia BednarskaAlicja WieczorekDariusz Gawin
Lodz University of Technology, Department of Building Physics and Building Materials, Poland
Show Author Information

Graphical Abstract

Abstract

In the analyses of the uncertainty propagation of buildings' energy-demand, the Monte Carlo method is commonly used. In this study we present two alternative approaches: the stochastic perturbation method and the transformed random variable method. The energy-demand analysis is performed for the representative single-family house in Poland. The investigation is focused on two independent variables, considered as uncertain, the expanded polystyrene thermal conductivity and external temperature; however the generalization on any countable number of parameters is possible. Afterwards, the propagation of the uncertainty in the calculations of the energy consumption has been investigated using two aforementioned approaches. The stochastic perturbation method is used to determine the expected value and central moments of the energy consumption, while the transformed random variable method allows to obtain the explicit form of energy consumption probability density function and further characteristic parameters like quantiles of energy consumption. The calculated data evinces a high accordance with the results obtained by means of the Monte Carlo method. The most important conclusions are related to the computational cost reduction, simplicity of the application and the appropriateness of the proposed approaches for the buildings' energy-demand calculations.

Electronic Supplementary Material

Download File(s)
bs-15-3-357_ESM.pdf (984.7 KB)

References

 

Allard I, Olofsson T, Nair G (2018). Energy evaluation of residential buildings: Performance gap analysis incorporating uncertainties in the evaluation methods. Building Simulation, 11: 725–737.

 

Allouhi A, El Fouih Y, Kousksou T, et al. (2015). Energy consumption and efficiency in buildings: current status and future trends. Journal of Cleaner Production, 109: 118–130.

 

Aude P, Tabary L, Depecker P (2000). Sensitivity analysis and validation of buildings' thermal models using adjoint-code method. Energy and Buildings, 31: 267–283.

 

Belazi W, Ouldboukhitine S-E, Chateauneuf A, et al. (2018). Uncertainty analysis of occupant behavior and building envelope materials in office building performance simulation. Journal of Building Engineering, 19: 434–448.

 

Catalina T, Virgone J, Blanco E (2008). Development and validation of regression models to predict monthly heating demand for residential buildings. Energy and Buildings, 40: 1825–1832.

 

Coakley D, Raftery P, Keane M (2014). A review of methods to match building energy simulation models measured data. Renewable and Sustainable Energy Reviews, 37: 123–141.

 

Crawley DB, Lawrie LK, Winkelmann FC, et al. (2001). EnergyPlus: creating a new-generation building energy simulation program. Energy and Buildings, 33: 319–331.

 

de Wit S, Augenbroe G (2002). Analysis of uncertainty in building design evaluations and its implications. Energy and Buildings, 34: 951–958.

 

Ding C, Cui X, Deokar R, et al. (2018). Modeling and simulation of steady heat transfer analysis with material uncertainty: Generalized nth order perturbation isogeometric stochastic method. Numerical Heat Transfer, PartA: Applications, 74: 1565–1582.

 

Domínguez-Muñoz F, Anderson B, Cejudo-López JM, et al. (2010). Uncertainty in the thermal conductivity of insulation materials. Energy and Buildings, 42: 2159–2168.

 

Eisenhower B, O'neill Z, Fonoberov VA, et al. (2012). Uncertainty and sensitivity decomposition of building energy models. Journal of Building Performance Simulation, 5: 171–184.

 
EN 10456 (2007). Building Materials and Products. Hygrothermal Properties. Tabulated Design Values and Procedures for Determining Declared and Design Thermal Values.
 

Heeren N, Mutel CL, Steubing B, et al. (2015). Environmental impact of buildings—What matters? Environmental Science & Technology, 49: 9832–9841.

 

Heiselberg P, Brohus H, Hesselholt A, et al. (2009). Application of sensitivity analysis in design of sustainable buildings. Renewable Energy, 34: 2030–2036.

 

Heo Y, Choudhary R, Augenbroe GA (2012). Calibration of building energy models for retrofit analysis under uncertainty. Energy and Buildings, 47: 550–560.

 

Hien TD, Kleiber M (1997). Stochastic finite element modelling in linear transient heat transfer. Computer Methods in Applied Mechanics and Engineering, 144: 111–124.

 

Hien TD, Kleiber M (1998). On solving nonlinear transient heat transfer problems with random parameters. Computer Methods in Applied Mechanics and Engineering, 151: 287–299.

 

Hopfe CJ, Hensen JLM (2011). Uncertainty analysis in building performance simulation for design support. Energy and Buildings, 43: 2798–2805.

 
IES (2017). The Institute of Environmental Economics (IES). Energy Efficiency in Poland–2017 review.
 

Kamiński M, Hien TD (1999). Stochastic finite element modeling of transient heat transfer in layered composites. International Communications in Heat and Mass Transfer, 26: 801–810.

 

Kamiński M (2010). Generalized stochastic perturbation technique in engineering computations. Mathematical and Computer Modelling, 51: 272–285.

 

Kamiński M, Strąkowski M (2017). On sequentially coupled thermo-elastic stochastic finite element analysis of the steel skeletal towers exposed to fire. European Journal of Mechanics—A/Solids, 62: 80–93.

 

Kershaw T, Eames M, Coley D (2011). Assessing the risk of climate change for buildings: A comparison between multi-year and probabilistic reference year simulations. Building and Environment, 46: 1303–1308.

 

Kim Y-J, Ahn K-U, Park C-S (2014). Decision making of HVAC system using Bayesian Markov chain Monte Carlo method. Energy and Buildings, 72: 112–121.

 
Kleiber M, Hien TD (1992). The Stochastic Finite Element Method: Basic Perturbation Technique and Computer Implementation. Chichester, UK: John Wiley & Sons.
 
Krysicki W, Bartos J, Dyczka W, et al. (2007). Probability Theory and Mathematical Statistics. Warsaw: PWN Warszawa. (in Polish)
 
NAPE (2012). Polish Building Typology–TABULA–Scientific report. National Energy Conservation Agency (NAPE).
 

Prada A, Cappelletti F, Baggio P, Gasparella A (2014). On the effect of material uncertainties in envelope heat transfer simulations. Energy and Buildings, 71: 53–60.

 

Prada A, Pernigotto G, Baggio P, et al. (2018). Uncertainty propagation of material properties in energy simulation of existing residential buildings: The role of buildings features. Building Simulation, 11: 449–464.

 
Pugachev VS (1984). Probability Theory and Mathematical Statistics for Engineers. Oxford, UK: Pergamon Press.
 

Rivalin L, Stabat P, Marchio D, et al. (2018). A comparison of methods for uncertainty and sensitivity analysis applied to the energy performance of new commercial buildings. Energy and Buildings, 166: 489–504.

 

Silva AS, Ghisi E (2014). Uncertainty analysis of the computer model in building performance simulation. Energy and Buildings, 76: 258–269.

 

Wu F, Zhong WX (2016). A modified stochastic perturbation method for stochastic hyperbolic heat conduction problems. Computer Methods in Applied Mechanics and Engineering, 305: 739–758.

 

Yang TJ, Cui XY (2017). A random field model based on nodal integration domain for stochastic analysis of heat transfer problems. International Journal of Thermal Sciences, 122: 231–247.

 

Yi H, Braham WW (2015). Uncertainty characterization of building emergy analysis (BEmA). Building and Environment, 92: 538–558.

 

Zhai Z, Helman JM (2019). Climate change: Projections and implications to building energy use. Building Simulation, 12: 585–596.

Building Simulation
Pages 357-371
Cite this article:
Koniorczyk M, Grymin W, Zygmunt M, et al. Stochastic energy-demand analyses with random input parameters for the single-family house. Building Simulation, 2022, 15(3): 357-371. https://doi.org/10.1007/s12273-021-0812-9

553

Views

22

Downloads

8

Crossref

7

Web of Science

6

Scopus

1

CSCD

Altmetrics

Received: 09 February 2021
Revised: 13 April 2021
Accepted: 29 April 2021
Published: 14 July 2021
© The Author(s) 2021

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/

Return