Abstract
This paper investigates the influence of a stochastic variation of both energy and economic parameters in an optimization loop applied to a refurbished social housing building. Usually, energy and economic optimization procedures rely on the results of an underlying numerical deterministic model which influences both energy gains and economic figures. However, an analyst must always face the random variation of input and parameter data. The unknown data can represent poor initial information or data that can change in a long time; this is the case of fuel cost and economic indexes in particular. This paper deals with both problems for building refurbishment optimization, the former related to the initial state of a building, and the latter to the energy cost variability. Reliability analysis considers a stochastic variation of parameters looking for solutions that incorporate a risk level; in this case, it deals with optimization objectives related to different impacts on economic, environmental and health aspects. The considered building represents a social house, and the energy reduction measures involve the application of internal insulation layers to the walls and the replacement of existing windows with more efficient ones.
Highlights
Energy consumption in the residential sector in Italy covers 36% of the national final energy use, a large amount, especially if compared to the transport sector that absorbs 32%, and the industrial sector, responsible for a 23% share [1]
This paper investigates the influence of a stochastic variation of both energy and economic parameters in an optimization loop applied to a refurbished social housing building
Once the net present value (NPV) percentiles have been computed, a reliability-based optimization (RBDO) can be performed by applying suitable optimization algorithms. modeFRONTIER allows us to operate with nested projects so this capability has been exploited to carry on the optimization: an external project carries on the true optimization step while an inner project performs the polynomial chaos expansion on the economic computation, step while an inner project performs the polynomial chaos expansion on the economic computation, providing the external one with the 10th percentile value to be used as objective
Summary
Energy consumption in the residential sector in Italy covers 36% of the national final energy use, a large amount, especially if compared to the transport sector that absorbs 32%, and the industrial sector, responsible for a 23% share [1]. Leave-one-out R-square, which consists of iteratively leaving one design out of the training set and evaluating the R-square index by training the regression model with the remaining part of the set, and averaging the results. It can be proved [29] that the convergence rate to the exact momentum of distributions using polynomial chaos regression is exponential with the number of samples, assuring a high accuracy by a low number of sampling points evaluation
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