Invariant probabilistic sensitivity analysis for building energy models. Issue 4 (4th July 2017)

• Record Type: Journal Article
• Authors: Zhang, B.; Liu, Yangbo; Rai, Rahul; Krovi, Venkat
• Is Part Of: Journal of building performance simulation. Volume 10:Issue 4(2017)
• Page Start: 392
• Page End: 405
• Publication Date: 2017-07-04
• Subjects: probabilistic sensitivity analysis -- building energy models -- scale invariant heat kernel signature; 690.0113
• DOI: 10.1080/19401493.2016.1265590
• Languages: English
• ISSNs: 1940-1493

Abstract

Abstract : One of the key challenges in the field of building-performance analysis is to identify the key source of input parameter/s that has/have largest effect on the energy efficiency of the buildings. Critical input parameters can be identified through the use of sensitivity analysis models. In this paper, we outline an invariant probabilistic sensitivity analysis technique to enable screening of the most important parameters in the building energy models. The proposed probabilistic sensitivity analysis method measures the average distance between unconditional probability distribution and conditional (on an input) probability distribution of an output. The distribution's description is based on the scale-invariant heat kernel signature (SI-HKS). In this paper, SI-HKS method is extended for invariant probabilistic sensitivity analysis. We demonstrate the application of the proposed method through the use of Energy Plus building-performance modelling tool.