Deep Sparse Coding for Non–Intrusive Load Monitoring
Deep Sparse Coding for Non–Intrusive Load Monitoring
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DOI：
10.1109/tsg.2017.2666220
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发表时间：
2018-09
影响因子：
9.6
通讯作者：
Shikha Singh;A. Majumdar
中科院分区：
文献类型：
--
作者：
Shikha Singh;A. Majumdar
Energy disaggregation is the task of segregating the aggregate energy of the entire building (as logged by the smartmeter) into the energy consumed by individual appliances. This is a single channel (the only channel being the smart-meter) blind source (different electrical appliances) separation problem. The traditional way to address this is via stochastic finite state machines (e.g., factorial hidden Markov model). In recent times, dictionary learning-based approaches have shown promise in addressing the disaggregation problem. The usual technique is to learn a dictionary for every device and use the learned dictionaries as basis for blind source separation during disaggregation. Prior studies in this area are shallow learning techniques, i.e., they learn a single layer of dictionary for every device. In this paper, we propose a deep learning approach—instead of learning one level of dictionary, we learn multiple layers of dictionaries for each device. These multi-level dictionaries are used as a basis for source separation during disaggregation. Results on two benchmark datasets and one actual implementation show that our method outperforms state-of-the-art techniques.
