Residential households in the EU are estimated to have a savings potential of around 27% [1]. The question yet remains on how to realize this savings potential. Non-Intrusive Appliance Load Monitoring (NIALM) aims to disaggregate the combination of household appliance energy signals with only measurements of the total household power load. The core of this thesis has been the implementation of an extension to a Gibbs sampling model with Hidden Markov Models for energy disaggregation. The goal has been to improve overall performance, by including the duration times of electrical appliances in the probabilistic model. The final algorithm was evaluated in comparison to the base algorithm, but results remained at the very best inconclusive, due to the model's inherent limitations. The work was performed at the Swedish company Watty. Watty develops the first energy data analytic tool that can automate the energy efficiency process in buildings.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-140840 |
| Date | January 2014 |
| Creators | Monin Nylund, Jean-Alexander |
| Publisher | KTH, Matematisk statistik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | TRITA-MAT-E ; 2014:10 |
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