Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Uncertainty analysis quantifies the inherently uncertain nature of building energy performance, whereas sensitivity analysis identifies key factors to explain variations in building energy performance. With the ability to handle complex relationships, machine learning techniques offer an effective approach to more accurate and reliable uncertainty and sensitivity analysis. This paper provides valuable insights into the current state and future prospects of machine learning-based uncertainty and sensitivity analysis for building energy performance. The development of machine learning-based uncertainty analysis is discussed from three perspectives: observational data-based probabilistic prediction, surrogate model-based uncertainty quantification, and inverse uncertainty quantification. Variance-based sensitivity analysis using surrogate machine learning models decomposes output variance associated with each input. In contrast, machine learning-based variable importance refers to the change of model predictive performance using model-specific or model-agnostic approaches. Finally, future research directions on machine learning-based uncertainty and sensitivity analysis of building energy performance are presented.
Uncertainty analysis quantifies the inherently uncertain nature of building energy performance, whereas sensitivity analysis identifies key factors to explain variations in building energy performance. With the ability to handle complex relationships, machine learning techniques offer an effective approach to more accurate and reliable uncertainty and sensitivity analysis. This paper provides valuable insights into the current state and future prospects of machine learning-based uncertainty and sensitivity analysis for building energy performance. The development of machine learning-based uncertainty analysis is discussed from three perspectives: observational data-based probabilistic prediction, surrogate model-based uncertainty quantification, and inverse uncertainty quantification. Variance-based sensitivity analysis using surrogate machine learning models decomposes output variance associated with each input. In contrast, machine learning-based variable importance refers to the change of model predictive performance using model-specific or model-agnostic approaches. Finally, future research directions on machine learning-based uncertainty and sensitivity analysis of building energy performance are presented.
Towards advanced uncertainty and sensitivity analysis of building energy performance using machine learning techniques
Tian, Wei (Autor:in)
Journal of Building Performance Simulation ; 17 ; 655-662
01.11.2024
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Advanced machine learning techniques for building performance simulation: a comparative analysis
| Taylor & Francis Verlag | 2019
Uncertainty and sensitivity decomposition of building energy models
| Taylor & Francis Verlag | 2012
Towards Practical Machine Learning Techniques
| British Library Conference Proceedings | 1994
Uncertainty and sensitivity analysis techniques for hydrologic modeling
| British Library Online Contents | 2009