Document Type
Conference Paper
Rights
Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence
Disciplines
Computer Sciences
Abstract
Providing domestic energy consumers with a detailed breakdown of their electricity consumption, at the appliance level, empowers the consumer to better manage that consumption and reduce their over- all electricity demand. Non-Intrusive Load Monitoring (NILM) is one method of achieving this breakdown and makes use of one sensor which measures overall combined electricity usage. As all appliances are measured in combination in NILM this consumption must be disaggregated to extract appliance level consumption. Machine learning techniques can be adopted to perform this disaggregation with various levels of accuracy, with Hidden Markov Model (HMM) derivatives ordering among the most accurate results. This work investigates how sensor sampling rate affects disaggregation accuracy obtained through HMM.
DOI
https://doi.org/10.21427/dvf8-nn35
Recommended Citation
Lynch, S. & Longo, L. (2017). On the Relationship Between Sampling Rate and Hidden Markov Models Accuracy in Non-intrusive Load Monitoring. Irish Conference on Artificial Intelligence and Cognitive Science, December, Dublin, Ireland. doi:10.21427/dvf8-nn35
Publication Details
https://dblp.org/db/series/ceurws/ceurws2300-2399.html