Energy monitoring with a self-taught deep network
Energy usage is a significant part of daily life, so the ability to monitor this use offers a number of benefits, from cost savings to improved safety. A key challenge is the lack of labeled data. Yiqun Hu shares a new solution: a RNN-based network trained to learn good features from unlabeled data.
| Talk Title | Energy monitoring with a self-taught deep network |
| Speakers | Yiqun Hu (Singapore Power) |
| Conference | Strata + Hadoop World |
| Conf Tag | Make Data Work |
| Location | Singapore |
| Date | December 6-8, 2016 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
Energy usage is a significant part of daily life, so the ability to monitor this use offers a number of benefits, from cost savings to improved safety. A key challenge is the lack of labeled data. Yiqun Hu shares a new solution: a RNN-based network trained to learn good features from unlabeled data. The self-learned feature extractor can then be used for different downstream applications, such as appliance detection (supervised) and anomaly detection (unsupervised).
