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| Author: | Lange, Henning |
| Title: | Disaggregation by State Inference A Probabilistic Framework For Non-Intrusive Load Monitoring |
| Publication type: | Master's thesis |
| Publication year: | 2015 |
| Pages: | 58 Language: eng |
| Department/School: | Perustieteiden korkeakoulu |
| Main subject: | Informaatiotekniikka (T-61) |
| Supervisor: | Karhunen, Juha |
| Instructor: | Berges, Mario |
| Electronic version URL: | http://urn.fi/URN:NBN:fi:aalto-201602161334 |
| Location: | P1 Ark Aalto 3503 | Archive |
| Keywords: | hidden Markov models energy disaggregation load monitoring efficient inference |
| Abstract (eng): | Non-intrusive load monitoring (NILM), the problem of disaggregating whole home power measurements into single-appliance measurements, has received increasing attention from the academic community because of its energy saving potentials, however the majority of NILM approaches are either variants of event-based or event-less disaggregation. Event-based approaches are able to capture much information about the transient behavior of appliances but suffer from error-propagation problems whereas event-less approaches are lessprone to error-propagation problems but can only incorporate transient information to a small degree. On top of that inference techniques for event-less approaches are either computationally expensive, do not allow to trade off computational time for approximation accuracy or are prone to local minima. This work will contribute three-fold: first an automated way to infer ground truth from single appliance readings is introduced, second an augmentation for event-less approaches is introduced that allows to capture side-channel as well as transient information of change-points, third an inference technique is presented that allows to control the trade-off between computational expense and accuracy. Ultimately, this work will try to put the NILM problem into a probabilistic framework that allows for closing feedback loops between the different stages of event-based NILM approaches, effectively bridging event-less and event-based approaches. The performance of the inference technique is evaluated on a synthetic data set and compared to state-of-the-art approaches. Then the hypothesis that incorporating transient information increases the disaggregation performance is tested on a real-life data set. |
| ED: | 2016-02-21 |
INSSI record number: 53126
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