A machine learning algorithm to improve building performance modeling during design

Chanachok Chokwitthaya; Yimin Zhu; Robert Dibiano; Supratik Mukhopadhyay

doi:10.1016/j.mex.2019.10.037

A machine learning algorithm to improve building performance modeling during design

MethodsX. 2019 Nov 2:7:35-49. doi: 10.1016/j.mex.2019.10.037. eCollection 2020.

Authors

Chanachok Chokwitthaya¹, Yimin Zhu¹, Robert Dibiano², Supratik Mukhopadhyay³

Affiliations

¹ Department of Construction Management, Louisiana State University, Baton Rouge 70803, USA.
² Ailectric LLC., 7117 Florida Blvd, Baton Rouge 70806, USA.
³ Department of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge 70803, USA.

Abstract

Building design involves the optimization of factors affecting building performance such as building functions, comfort, safety, and energy. Building performance models (BPMs) help designers to evaluate and optimize such factors. However, the lack of design capabilities to validly describe human-building interactions for buildings under design may contribute to the development of inaccurate BPMs and the performance discrepancy between predictions and actual buildings. To address this challenge, a computational framework is proposed to increase the estimations performance of BPMs. The framework uses artificial neural networks (ANNs) to combine an existing BPM and context-aware design-specific data describing design-specific human-building interactions captured by using immersive virtual environments (IVEs). The framework produces an augmented BPM that can predict building performance taking human-building interactions specific to a new design into consideration. It incorporates a feature ranking technique allowing designers to assess impacts of contextual factors on human-building interactions. The paper focuses on providing details of theories, experiment and data collection designs, and algorithms behind the framework as a companion paper of [1]. •A framework for combining contextual factors with building performance models to enhance their predictive performance.•Computation for determining impacts of contextual factors on human-building interaction.

Keywords: A framework for combining context-aware design-specific data and building performance models to improve building performance predictions during design; Artificial neural network; Building performance models; Contextual factors; Feature ranking; Immersive virtual environments; Occupant behaviors.