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).
Yiqun Hu is the head of data for SP Digital, where he is responsible for leading the data team on the development of machine learning capabilities for energy and utility applications. Previously, he helped several organizations build data-driven products such as image recognition systems and recommendation engines. Yiqun is the author of 30+ scientific publications in the machine learning area, with over 1,400 citations. He holds a PhD from Nanyang Technological University and a bachelor’s degree in computer science from Xiamen University.
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