A Comparative Analysis of Multi-Target Feature Selection Methods in Data-Driven Models for Building Energy and Thermal Performance Prediction
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Date
2024
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
Building energy management increasingly utilises Machine Learning (ML) to use data from sensor-rich environments. A significant challenge in this context is managing high-dimensional data, which can affect model performance. This study addresses this by applying multi-target feature selection, an underexplored method that reduces dimensionality by analysing inter-feature relationships. From 182 features, two were key for developing three ML models predicting the energy and thermal performance of the HiLo living lab. These models achieved a robust fit with an average Root Mean Squared Error (RMSE) of 0.18 kW and 1.03 °C, demonstrating multi-target feature selection’s effectiveness in enhancing building performance predictions.
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Publication status
published
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Book title
Proceedings of the 2024 European Conference on Computing in Construction
Journal / series
Volume
5
Pages / Article No.
91 - 97
Publisher
European Council on Computing in Construction
Event
2024 European Conference on Computing in Construction (EC³)
Edition / version
Methods
Software
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Date collected
Date created
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Organisational unit
03902 - Schlüter, Arno / Schlüter, Arno