Transparent machine learning models for predicting decisions to undertake energy retrofits in residential buildings
Author:
Publisher
Springer Science and Business Media LLC
Subject
Management Science and Operations Research,General Decision Sciences
Link
https://link.springer.com/content/pdf/10.1007/s10479-023-05217-5.pdf
Reference97 articles.
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3. Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052
4. Ahady Dolatsara, H., Chen, Y. J., Evans, C., Gupta, A., & Megahed, F. M. (2020). A two-stage machine learning framework to predict heart transplantation survival probabilities over time with a monotonic probability constraint. Decision Support Systems, 137, 113363. https://doi.org/10.1016/j.dss.2020.113363
5. Alberini, A., Banfi, S., & Ramseier, C. (2013). Energy efficiency investments in the home: Swiss homeowners and expectations about future energy prices. The Energy Journal, 34(1), 49–86. https://doi.org/10.5547/01956574.34.1.3
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