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.
Highlights
A major challenge in estimating building’s lifecycle energy consumption is how to adequately address the longitudinal variations of the parameters in the lifecycle energy consumption model
The existing models using neural network approach have significant limitations when applied to building’s lifecycle energy consumption due to the lack of models to address the longitudinal variations of the input parameters, which are stochastic in nature (Mc Duling 2006; Hussain, Ansari 2010; Wang et al 2011b; de Wilde et al 2011)
1) The practical service lifetime of the building and the expected building condition at specific time are predicted by the developed building Markov deterioration model based on the historical condition record of similar buildings; 2) The future temperature condition is estimated by Markov Chain model based on the local historical weather record; 3) The annual energy consumption variation is simulated as a joint process of the building deterioration and temperature change; 4) To calculate annual energy consumption, the corresponding energy consumption probabilistic distribution for each joint state is estimated by neural network with the above available data set
Summary
The existing models using neural network approach have significant limitations when applied to building’s lifecycle energy consumption due to the lack of models to address the longitudinal variations of the input parameters (e.g. future residential condition, future ambient climate status, etc.), which are stochastic in nature (Mc Duling 2006; Hussain, Ansari 2010; Wang et al 2011b; de Wilde et al 2011). The detailed procedure of expected-value method for deriving TPM can be processed as follows (Carnahan et al 1987; Ortiz-Garcia et al 2006; Madanat et al 1995): Step 1: Choosing the residential buildings similar to the targeted one from data pool This takes into account the fact that residential deterioration rate is a function of several previously stated explanatory variables (factors). It is assumed that the building degradation process is continuous from the long trend perspective
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