Towards a methodology to include building energy simulation uncertainty in the Life Cycle Cost analysis of rehabilitation alternatives

被引:19
作者
School of Technology and Management, Department of Civil Engineering, Campus Politécnico de Repeses, Viseu [1 ]
3504-510, Portugal
不详 [2 ]
4200-465, Portugal
机构
[1] School of Technology and Management, Department of Civil Engineering, Campus Politécnico de Repeses, Viseu
[2] University of Porto, Faculty of Engineering, Department of Civil Engineering, Rua Dr. Roberto Frias s/n, Porto
来源
J. Build. Eng. | / 44-51期
关键词
Energy simulation; Life Cycle Cost; Monte Carlo method; Uncertainty;
D O I
10.1016/j.jobe.2015.04.005
中图分类号
学科分类号
摘要
The selection of the best alternative for building rehabilitation should involve LCC analysis to account for all the costs involved. A significant part of those costs relates to energy consumption, which can only be assessed with an intrinsic level of uncertainty. This work proposes an integrated methodology that can quantify and integrate that uncertainty in LCC estimation. The methodology relies on Monte Carlo simulation to calculate statistical distributions of energy demand. The associated costs can then be introduced in an LCC analysis and provide decision makers with a measure of rehabilitation alternatives economic impact uncertainty. The paper describes the methodology and applies it to an example case. The results are mainly intended to illustrate the methodology application and pinpoint key aspects such as input data pre-processing, convergence analysis, and adequate economic measures. The methodology is not ready for a generalized application as reliable stochastic input data are not frequently available yet. Nevertheless, the results found in this work showed how this approach can influence decisions if the robustness of each alternative is known. © 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:44 / 51
页数:7
相关论文
共 30 条
[1]  
Fuller S.K., Petersen S.R., NIST Handbook 135: Life-Cycle Costing Manual for the Federal Energy Management Program, (1996)
[2]  
Hung M.-L., Ma W.H., Quantifying system uncertainty of life cycle assessment based on Monte Carlo simulation, Int. J. Life Cycle Assess., 14, pp. 19-27, (2009)
[3]  
Cabeza L.F., Rincon L., Vilarino V., Perez G., Castell A., Life cycle assessment (LCA) and life cycle energy analysis (LCEA) of buildings and the building sector: A review, Renew. Sustain. Energy Rev., 29, pp. 394-416, (2014)
[4]  
Brohus H., Frier C., Heiselberg P., Haghighat F., Quantification of uncertainty in predicting building energy consumption: A stochastic approach, Energy Build., 55, pp. 127-140, (2012)
[5]  
Tian W., Choudhary R., A probabilistic energy model for non-domestic building sectors applied to analysis of school buildings in greater London, Energy Build., 54, pp. 1-11, (2012)
[6]  
Santos P., Gervasio H., Da Silva L.S., Lopes A.G., Influence of climate change on the energy efficiency of light-weight steel residential buildings, Civil Eng. Environ. Syst., 28, pp. 325-352, (2011)
[7]  
Hopfe C.J., Hensen J.L.M., Uncertainty analysis in building performance simulation for design support, Energy Build., 43, pp. 2798-2805, (2011)
[8]  
Calleja Rodriguez G., Carrillo Andres A., Dominguez Munoz F., Cejudo Lopez J.M., Zhang Y., Uncertainties and sensitivity analysis in building energy simulation using macroparameters, Energy Build., 67, pp. 79-87, (2013)
[9]  
Cellura M., Longo S., Mistretta M., Sensitivity analysis to quantify uncertainty in life cycle assessment: The case study of an Italian tile, Renew. Sustain. Energy Rev., 15, pp. 4697-4705, (2011)
[10]  
Hoxha E., Habert G., Chevalier J., Bazzana M., Le Roy R., Method to analyse the contribution of material's sensitivity in buildings' environmental impact, J. Clean. Prod., 66, pp. 54-64, (2014)