Sensitivity analysis identifies how uncertainties in input parameters affect important measures of building performance, such as cost, indoor thermal comfort, or CO2 emissions. Input parameters for buildings fall into roughly three categories: * Discrete design alternatives, e.g. different glazing options, number of storeys, etc. * Variance in physical parameters such as U-values, air tightness and location of leakages, and variance/uncertainty in economic parameters such as interest rate, energy prices, or service-life. * Stochastic behaviour-related parameters such as occupancy pattern (number, timing, and location), and use of hot water, window airing, lighting and electrical equipment. Differing personal preferences for air temperature and lighting level. Each parameter has a different distribution of possible values. Sensitivity analysis is an effective way of identifying which parameters influence simulation results the most, and thus need more attention during design. More specifically, sensitivity analysis qualifies how much each parameter affects the results, either individually or in combination (synergistic or antagonistic), and quantifies the variance in possible outcomes, such as energy costs, and is thus a very powerful quantitative tool for decision making. (Wikipedia).
Overview of various methods for sensitivity analysis in the UQ of subsurface systems
From playlist Uncertainty Quantification
Sensitivity (Electrical Engineering)
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From playlist Advanced Circuit Design
The tool that engineers use to design buildings in earthquake zones | The response spectrum
Earthquakes are one of the most destructive forces of nature. They could induce substantial movement in the ground, which results in the development of excessive forces in structural components, resulting in their failure. The intent of the analysis is to somehow predict the **maximum resp
From playlist Summer of Math Exposition Youtube Videos
Sensitivity Analysis for Financial Models in Excel
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From playlist Excel Tutorials
http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Representation of wide sense stationary random processes in the frequency domain - the power spectral density or power spectrum is the DTFT of the a
From playlist Random Signal Characterization
Chapter 12 Sensitivity Specificity Predictive Values Odds Ratios
Ever wandered how to calculate sensitivity, specificity, positive and negative predictive values or odds ratios or even simply what these terms mean? Watch this short lecture.
From playlist Medical Statistics
MLE+ is an open-source Matlab/Simulink toolbox for co-simulation with the whole-building energy simulator EnergyPlus. It is designed for engineers and researchers who are familiar with Matlab and Simulink and want to use these software tools in building energy simulation.
From playlist Simulink Student Challenge 2012 Entries
Conservation of Energy | Lecture 45 | Vector Calculus for Engineers
Derivation of the conservation of energy from the work-energy theorem and a conservative vector field. Join me on Coursera: https://www.coursera.org/learn/vector-calculus-engineers Lecture notes at http://www.math.ust.hk/~machas/vector-calculus-for-engineers.pdf Subscribe to my channel
From playlist Vector Calculus for Engineers
Prob & Stats - Bayes Theorem (2 of 24) What is the Sensitivity of a Test?
Visit http://ilectureonline.com for more math and science lectures! In this video I will explain what is and give examples of the sensitivity of a test. The sensitivity of a test indicates the probability that the subject will have a POSITIVE result when the subject is actually POSITIVE.
From playlist PROB & STATS 4 BAYES THEOREM
Bayesian Evidential Learning a protocol for uncertainty quantification in Earth systems
Webinar for CSDMS, Oct 14, 2019
From playlist Bayesian Evidential Learning
04-2 Sensitivity Analysis Global
Sobol' and regionalized sensitivity analysis
From playlist QUSS GS 260
04-3 Sensitivity Analysis Trees
Sensitivity analysis using classification and regression trees
From playlist QUSS GS 260
DDPS | Parameter Subset Selection and Active Subspace Techniques for Engineering & Biological Models
Engineering and biological models generally have a number of parameters which are nonidentifiable in the sense that they are not uniquely determined by measured responses. Furthermore, the computational cost of high-fidelity simulation codes often precludes their direct use for Bayesian m
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
Short introduction to Bayesian Evidential Learning: a protocol for uncertainty quantification
From playlist Bayesian Evidential Learning
Parametric vs Nonparametric Spectrum Estimation
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Introduces parametric (model-based) and nonparametric (Fourier-based) approaches to estimation of the power spectrum.
From playlist Estimation and Detection Theory
Quantifying Uncertainty in Subsurface Systems
Presentation based on the book published by Wiley Scheidt, C., Li, L & Caers, J, 2018. "Quantifying Uncertainty in Subsurface Systems.
From playlist Uncertainty Quantification