Name
Abstract | ||
|---|---|---|
Non-intrusive Load Monitoring (NILM) is a hallmark of monitoring technologies. It provides appliance-level energy usage feedback with minimal sensor deployments. How-ever, existing NILM systems are not designed with embedded systems (e.g. Smart Meters) in mind. In this paper, we consider a Bayesian solution which efficiently computes the Log-Likelihood Ratio (LLR) of an appliance's on/off state, then combines it with historical estimates to yield a new estimate for improved accuracy. The detection of multiple appliances is achieved through an iterative method which also deals with unidentified appliances. With minimal multiplication and division, the algorithm is computationally lightweight and can easily be implemented in embedded systems using low-power processors. Despite the simplicity, results show promising disaggregation performance. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CIASG.2013.6611502 | CIASG |
Keywords | Field | DocType |
appliance-level energy usage feedback,llr,bayesian solution,sensor deployments,nilm,bayes methods,smart meters,log-likelihood ratio,iterative method,multiple appliance detection,load forecasting,low-power processors,nonintrusive load monitoring,embedded systems,iterative methods,soldering,estimation,steady state,mathematical model,log likelihood ratio | Metre,Iterative method,Algorithm,Real-time computing,Load forecasting,Multiplication,Engineering,Bayesian probability | Conference |
ISSN | Citations | PageRank |
2326-7682 | 1 | 0.34 |
References | Authors | |
10 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Voon Siong Wong | 1 | 1 | 0.68 |
| Yung Fei Wong | 2 | 1 | 0.68 |
| Tom Drummond | 3 | 2676 | 159.45 |
| Y. Ahmet Sekercioglu | 4 | 289 | 22.73 |