| Publication Type |
poster |
| School or College |
College of Engineering |
| Department |
Mechanical Engineering |
| Creator |
Rahman, Aowabin |
| Other Author |
Smith, Amanda D. |
| Title |
Deep recurrent neural networks for building energy prediction |
| Date |
2017-01-13 |
| Description |
This poster illustrates the development of a deep recurrent neural network (RNN) model using long-short-term memory (LSTM) cells to predict energy consumption in buildings at one-hour time resolution over medium-to-long term time horizons ( greater than or equal to 1 week). |
| Type |
Text |
| Publisher |
University of Utah |
| Subject |
Machine learning; Energy; Building energy modeling; Deep learning; Recurrent neural networks; Prediction |
| Language |
eng |
| Conference Title |
Utah Science Day, University of Utah, Salt Lake City, UT |
| Rights Management |
© Aowabin Rahman, Amanda D. Smith |
| Format Medium |
application/pdf |
| ARK |
ark:/87278/s68h2r92 |
| Setname |
ir_uspace |
| ID |
1349230 |
| Reference URL |
https://collections.lib.utah.edu/ark:/87278/s68h2r92 |
