Informing building retrofits at low computational costs: a multi-objective optimisation using machine learning surrogates of building performance simulation models
Author:
Affiliation:
1. Department of Civil and Environmental Engineering, Carleton University, Ottawa, Canada
2. Department of Civil Engineering, IIT Bombay, Powai, Mumbai, India
Funder
Canada Research Chairs
Department of Science and Technology, Ministry of Science and Technology, India
India-Canada Centre for Innovative Multidisciplinary Partnerships to Accelerate Community Transformation and Sustainability
Publisher
Informa UK Limited
Link
https://www.tandfonline.com/doi/pdf/10.1080/19401493.2024.2384487
Reference66 articles.
1. Optimizing building energy performance predictions: A comparative study of artificial intelligence models
2. Urban building energy performance prediction and retrofit analysis using data-driven machine learning approach
3. Machine learning as a surrogate to building performance simulation: Predicting energy consumption under different operational settings
4. A comprehensive framework to quantify energy savings potential from improved operations of commercial building stocks
5. Building energy optimization using surrogate model and active sampling
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