Machine learning for early stage building energy prediction: Increment and enrichment KU Leuven
Collecting data for machine learning (ML) development is a resource-intensive task that necessitates identifying an efficient data collection approach. This study focuses on ML models that provide quick energy results by dramatically reducing computational demand. The generalisation of such models for multiple building shapes is vital to early-stage energy prediction. Therefore, this article examines which approach of collecting new training ...