Towards advanced uncertainty and sensitivity analysis of building energy performance using machine learning techniques-Reference-Cited by-同舟云学术

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Towards advanced uncertainty and sensitivity analysis of building energy performance using machine learning techniques

Published:2024-08-02 Issue: Volume: Page:1-8
ISSN:1940-1493
Container-title:Journal of Building Performance Simulation
language:en
Short-container-title:Journal of Building Performance Simulation

Author:

Tian Wei¹²^ORCID

Affiliation:

1. Tianjin Key Laboratory of Integrated Design and On-line Monitoring for Light Industry & Food Machinery and Equipment, College of Mechanical Engineering, Tianjin University of Science and Technology, Tianjin, People’s Republic of China

2. Tianjin International Joint Research and Development Center of Low-Carbon Green Process Equipment, Tianjin, People’s Republic of China

Funder

National Natural Science Foundation of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/19401493.2024.2387071

Reference34 articles.

1. Urban building energy performance prediction and retrofit analysis using data-driven machine learning approach

2. Machine learning surrogate of physics-based building-stock simulator for end-use load forecasting

3. Improved calibration of building models using approximate Bayesian calibration and neural networks

4. Development and performance evaluation of a minimum input model calibration methodology for residential buildings

5. Uncertainty and parameter ranking analysis on summer thermal characteristics of the hydronic thermal barrier for low-energy buildings

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