Thursday, 28 October 2010

Energy vs Power in NIALM

After having a play around with some real data from a Plogg connected to my machine, I began to wonder, which is more useful, energy consumption or power demand recorded from my Plogg? I plotted both over the last week:

After a glance at these two graphs, it becomes apparent that energy is not a function of power over time. In fact, a reading of the power demand at an instant in time is recorded at each interval. However, all the energy used during the interval period is accounted for from the energy readings.

These properties have both advantages and disadvantages for appliance monitoring. A major disadvantage of recording power values at time instants is that activities not taking place at the time of the reading are missed. An example of this is the spike clearly visible on the energy graph, but not recorded on the power graph.

However, the power graph does have an advantage over the energy graph. Because no averaging is done over the time interval, the reading reflects the exact sum of the appliances' power demands. This makes NIALM an almost trivial task. This is not quite so easy on the energy graph, as changes in appliance state between readings are likely and an average of both states is therefore recorded by the following reading.

Monday, 25 October 2010

Topic discussion

I had a chat with Alex today about my topic and what direction to read into within the literature. Here's what we decided:
  • Most approaches in the literature focus upon highly detailed data from expensive metering equipment
  • We want to look at what we can find from less detailed detailed data, captured from inexpensive monitoring equipment
  • The characteristic we want to measure is energy (or power), not current, voltage etc.
  • The granularity of data that should be available is in the frequency range of one reading per second
  • We want to investigate how complete the coverage of appliances needs to be. Some expensive equipment claim to recognise 99% of appliances. Is 90% just as useful though?
  • We also want to investigate when it is most important to prompt the user to tag an unrecognised appliance
This week I'm going to focus on the machine learning fundamentals, which will ultimately be used to extract features from the data, and match these features to known signatures.

Friday, 22 October 2010

End of week 3

This week I have read a bunch more papers on NIALM, and collected five of the best papers which provide an overview of the topic. These overview papers are few and far between, as the vast majority of papers contain a tiny background section and an in depth description of a proposed technique.

I've also been to a few more lectures on sustainable energy and power systems analysis. The lectures from both modules seem quite interesting and I'm working through some exercises with Sam.

I have been really impressed with Mendeley reference manager application, and am now using it to share references with Sam and also to store my reference database in the cloud.

I've continued to work through the Intelligent Agents course material, with only two lecture's worth left. I hope to finish it early next week and move on to look at some machine learning material, and how it can be applied to NIALM.

I had a chat with Bhargav about his topic for his MSc project. As I understood it, his project is focusing on the application of machine learning techniques to data collected from sub-metered appliances, with the aim to provide automatic behavioural interventions to encourage energy savings. We discussed how research detailed heavily in the literature discusses modelling appliances as Finite State Machines, and how this high-level information could be useful in providing behavioural interventions.

Inspired by this thought, I wondered whether this would be apparent in my own data collected by a Plogg connected to my work computer and monitors. With a quick plot of the half-hourly power and energy consumption I could already see a number of different states from the power saving settings of my machine; when my computer was on, when it had turned my monitors off, when it had gone to sleep, and when it was off. The fact that these states are recognisable at the low-grained data of 30 minutes intervals of the load data has convinced me that it should be possible to construct an FSM for a specific computer, or even a generalised FSM for a number of computers.

Wednesday, 20 October 2010

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.

Monday, 18 October 2010

End of week 2

I've now set up my machine and got it ready for work. Didn't have too many problems, though I did need an HDMI to DVI converter for my second monitor, and also had to change the boot order to stop it trying to boot from the network.

I've attended a bunch of lectures this week from the Energy Sustainability course. I've decided to carry on with the modules 'Power Systems Analysis' and 'Sustainable Energy Systems, Resources and Usage' for now, but drop 'Conventional Generation Technology' as it's not too relevant to my topic. It appears that the remaining module, 'Introduction to Energy Technologies', was a one off lecture and will not be continued throughout the semester.

I've conducted a more thorough literature review on the existing solutions to the Appliance Load Monitoring problem. Starting from the PhD thesis' references and working backwards, I've collected as many relevant papers as I could find. The topic was introduced by Hart, in the 1980s, and is reviewed thoroughly in his 1992 paper 'Nonintrusive Appliance Load Monitoring'. Since then, many algorithmic approaches have been suggested for automatic appliance disaggregation, largely consisting of both pattern recognition and signature clustering.

I've experimented with some reference manager tools with limited success, but am gradually building up a bibtex file of any resources I've found.

I had a meeting with Mark in which we mostly discussed the topic and methods of literature search. He advised I start to take a more formalised approach, as opposed to the random path through the literature I'd been following so far. He's also lent me a book 'Persuasive Technology' by B. J. Fogg, which will be very useful when considering the HCI aspect of my topic.

I've also decided to work through the notes for Alex's Intelligent Agents module, since I didn't take this in my 4th year.

I'm still reading 'Sustainable Energy - without the hot air', and am now just over halfway through.

Thursday, 7 October 2010

First Week

This week has been the first week of my PhD, which has been mostly taken up my admin stuff. I've sorted out my computing account, got a desk and ordered a computer. I've also had a meeting with my supervisors, Mark and Alex, to define a topic, this was the outcome:

Investigation of collaborative human-agent appliance disambiguation from domestic energy use. Also, investigating the benefits of sharing the raw energy usage and derived recognition results over a social network.

I also attended one and a half days of compulsory induction material.

I am planning to attend some lectures from the MSc Energy Sustainability with Electrical Power Engineering, in addition to intensive courses on probability and game theory.

I have been reading parts of a PhD thesis titled 'Reducing Domestic Energy Consumption Through Behaviour Modification'. I've also been reading a couple of papers titled 'At the flick of a switch: Detecting and classifying unique electrical events on the residential power line' and 'Reducing domestic energy consumption: from psychology to technology'.

I have been reading the book 'Sustainable Energy - without the hot air' by David MacKay when I get the chance.