Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6727630 | Energy and Buildings | 2018 | 43 Pages |
Abstract
Moreover, an analysis based on the variable importance of RF was performed to identify the most influential features during different semesters. The results showed that the most influential features vary depending on the semester, indicating the existence of different operational conditions for the tested buildings. A further comparison between RF trained with yearly and monthly data indicated that the energy usage prediction for educational buildings could be improved by taking into consideration their energy behavior changes during different semesters.
Keywords
Related Topics
Physical Sciences and Engineering
Energy
Renewable Energy, Sustainability and the Environment
Authors
Wang Zeyu, Wang Yueren, Zeng Ruochen, Ravi S. Srinivasan, Sherry Ahrentzen,