Dyna-Bolt: Domain Adaptive Binary Factorization Of Current Waveforms For Energy Disaggregation
| Autor: | Mario Berges, Bingqing Chen, Henning Lange, Jingxiao Liu |
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| Rok vydání: | 2020 |
| Předmět: |
Training set
Computer science 05 social sciences Supervised learning Binary number Context (language use) 010501 environmental sciences computer.software_genre 01 natural sciences Domain (software engineering) Set (abstract data type) 0502 economics and business Metric (mathematics) Data mining Instrumentation (computer programming) 050207 economics computer Energy (signal processing) 0105 earth and related environmental sciences |
| Zdroj: | ICASSP |
| DOI: | 10.1109/icassp40776.2020.9054608 |
| Popis: | Non-intrusive load monitoring (NILM) is the set of algorithmic techniques for inferring the operational states of individual appliances in a household given the aggregate electrical measurements at a single point of instrumentation. Most successful techniques to-date approach the problem from a supervised learning perspective and thus rely on labeled data, which is costly to obtain, and assume similar data distributions for appliances beyond those in the training set. To alleviate this problem, we formulated NILM in a domain adaptation context. Using Binary OnLine FactorizaTion (BOLT) as the baseline model, we first demonstrate that direct application of domain adversarial training without application-specific modifications is unsuccessful, and hypothesize that this may be due to differences in the data distributions. We then propose Dyna-BOLT: a domain-adaptive variant of BOLT, in which we 1) provide private decoders for the source and target domains to account for the differences in current waveforms, and 2) tie the weights between the two decoders using a metric that was specifically trained to distinguish between appliance classes. We evaluate Dyna-BOLT on a publicly available dataset (REDD) and demonstrate that it compares favorably to unsupervised methods. |
| Databáze: | OpenAIRE |
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