A comparison of methods for uncertainty and sensitivity analysis applied to the energy performance of new commercial buildings

Abstract

A comparison of methods for uncertainty and sensitivity analysis applied to the energy performance of new commercial buildings Permalink https://escholarship.org/uc/item/0wd4303g Journal Energy and Buildings, 166 a b s t r a c t An Energy Performance Contracting (EPC) is a financing agreement offered by general contractors that enables cost savings from reduced energy consumption to building owners. To create such an offer, the contractor has to provide an energy consumption threshold and a measurement plan.This article aims to draw some recommendations to choose an appropriate approach to provide the information necessary to create the contract, regarding computation time budget, expected accuracy and type of information provided.To get these results, we couple thermal simulations to various uncertainty and sensitivity methods. We first compare screening and differential sensitivity to reduce the number of inputs of the statistical study. Then, we analyze various uncertainty analysis methods to set an appropriate energy consumption threshold, considering the input uncertainties and the study context (Quadratic combination, directional and importance sampling and reliability methods).Sensitivity analyses in various input spaces are then carried out to identify the most critical contributors to energy levels to create the measurement plan. Finally, two metamodeling approaches are tested to reduce the overall computational time: Kriging and sparse polynomial chaos.These methods are tested and compared on a 40 0 0 m ² office building in Nantes, France. The resulting recommendations can be applied to any building, depending on the model regularity, the number of uncertain parameters and the objective of the study.

Uncertainty and sensitivity analysis applied to a voltage series operational amplifier

“…The aim of the uncertainty analysis is to quantify the propagation of inputs and parameters uncertainties to the model outputs, whereas the sensitivity analysis quantifies the contribution of those inputs and parameters to the uncertainties [23,24]. The uncertainty and sensitivity analysis can be broadly classified into local and global techniques.…”

A new design exploring framework based on sensitivity analysis and Gaussian process regression in the early design stage

“…The importance of each parameter helped designers to make decisions in the latter stage. Nguyen and Reiter (2015), Rivalin et al (2018) and Gagnon, Gosselin, and Decker (2018) compared several SAtechnics applied to building energy performance assessment. Linear regression was reported not good enough by most researchers.…”

“…Asadi et al (2014) also combined ANN and GA in their optimization process and applied it in retrofit projects. Rivalin et al (2018), Wei et al (2015) and Østergård, Jensen, and Maagaard (2018) compared several techs of metamodeling applied to building energy performance assessment. Gaussian process was reported to be the most robust and easy-to use, neural network and multivariate adaptive regression splines also had good performance.…”

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

“…One can also define another type of the probabilistic methods' categorization, depending on the data we can obtain from the analyses. Namely, the distribution, dispersion and reliability uncertainty analyses might be distinguished (Rivalin et al 2018). Using the distribution analysis, one determines the full distribution of the random variable, the dispersion analysis provides mean, variance and central moments, whereas applying reliability analysis gives the probability of exceeding a threshold.…”