Abstract
Offers a framework to efficiently select machine learning models to forecast energy loads of buildings. Develops an energy performance prediction model for non-domestic buildings. Provides a case study showing the methodology at work
| Original language | English |
|---|---|
| Place of Publication | Cham, Switzerland |
| Publisher | Springer |
| Number of pages | 153 |
| Edition | 1 |
| ISBN (Electronic) | 978-3-030-64751-3 |
| ISBN (Print) | 978-3-030-64750-6 |
| Publication status | Published - 16 Jan 2021 |
Keywords
- energy performance
- retrofit planning
- modelling energy performance