Architectural Engineering and Construction, Durham School of

 

Date of this Version

10-24-2012

Citation

JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT ISSN 1392-3730 print/ISSN 1822-3605 online 2013 Volume 19(Supplement 1): S161–S171 doi:10.3846/13923730.2013.802744

Comments

2013 Vilnius Gediminas Technical University (VGTU)

Abstract

Accurate prediction of buildings’ lifecycle energy consumption is a critical part in lifecycle assessment of residential buildings. Longitudinal variations in building conditions, weather conditions and building’s service life can cause significant deviation of the prediction from the real lifecycle energy consumption. The objective is to improve the accuracy of lifecycle energy consumption prediction by properly modelling the longitudinal variations in residential energy consumption model using Markov chain based stochastic approach. A stochastic Markov model considering longitudinal uncertainties in building condition, degree days, and service life is developed: 1) Building’s service life is estimated through Markov deterioration curve derived from actual building condition data; 2) Neural Network is used to project periodic energy consumption distribution for each joint energy state of building condition and temperature state; 3) Lifecycle energy consumption is aggregated based on Markov process and the state probability. A case study on predicting lifecycle energy consumption of a residential building is presented using the proposed model and the result is compared to that of a traditional deterministic model and three years’ measured annual energy consumptions. It shows that the former model generates much narrower distribution than the latter model when compared to the measured data, which indicates improved result.

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