A Method for Selecting the Appropriate Source Domain Buildings for Building Energy Prediction in Transfer Learning: Using the Euclidean Distance and Pearson Coefficient

A Method for Selecting the Appropriate Source Domain Buildings for Building Energy Prediction in Transfer Learning: Using the Euclidean Distance and Pearson Coefficient

Building energy prediction faces challenges such as data scarcity while Transfer Learning (TL) demonstrates significant potential by leveraging source building energy data to enhance target building energy prediction. However, the accuracy of TL heavily relies on selecting appropriate source buildin...

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Bibliographic Details
Main Authors: Chuyi Luo, Liang Xia, Sung-Hugh Hong
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Energies
Subjects:
transfer learning
Pearson correlation coefficient
Euclidean distance
data-driven method
deep learning
long short-term memory
Online Access:https://www.mdpi.com/1996-1073/18/14/3706
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