Top papers for Non-Intrusive Appliance Load Monitoring (NIALM)

Update 09.09.2011 - I have since posted an updated list of NIALM papers.

Below are some of the most useful overview papers for NIALM.

1. Hart GW. Nonintrusive appliance load monitoring. Proceedings of the IEEE. 1992;80(12):1870-1891.

Comprehensive description of problem area and review of work to date. Although the approach implemented is now considered basic by today's standards, the outline of possible solutions is still applicable today.

2. Najmeddine H, El Khamlichi Drissi K, Pasquier C, et al. State of art on load monitoring methods. In: Power and Energy Conference, 2008. PECon 2008. IEEE 2nd International.; 2008:1256-1258.

A concise and up-to-date overview of the groups of approaches towards NIALM. Although short, this paper is useful because its focus is many existing techniques, as opposed to one newly proposed technique.

3. Laughman C, Lee K, Cox R, et al. Power signature analysis. Power and Energy Magazine, IEEE. 2003;1(2):56-63.

Detailed comparison of a number of NIALM approaches, with a discussion of their relative advantages and disadvantages. The effectiveness of the techniques within different setting, e.g. domestic, commercial, industrial is also evaluated. This paper is useful for the same reason as the last; the focus in on the range of techniques, not one specific implementation.

4. Matthews HS, Soibelman L, Berges M, Goldman E. Automatically Disaggregating the Total Electrical Load in Residential Buildings: a Profile of the Required Solution. Proc. Intelligent Computing in Engineering.381-389.

In depth overview of the practicalities of approaches proposed in the literature. Describes not only research methods in this area but also problems with modern commercial metering.

5. Ting KH, Lucente M, Fung GSK, Lee WK, Hui SYR. A Taxonomy of Load Signatures for Single-Phase Electric Appliances. In: IEEE PESC (Power Electronics Specialist Conference).; 2005.

Short discussion of why appliances present distinct signatures, and a proposal of how these signatures can be grouped. Since pattern recognition is generally improved through prior knowledge of appliances, the idea of identifying certain signatures out-of-the-box is an attractive one. However, the signatures described use a sampling of current and voltage at 50Hz, a data granularity far greater than that available from most modern smart meters.