Top 6 lists for Research Methodologies Course

Top 6 people:
G. W. Hart
M. E. Berges
M. Baranki
L. Farinaccio
S. B. Leeb
L. K. Norford

Top 6 conferences:
Ubiquitous Computing, ACM International Conference, ACM UbiComp
Autonomous Agents and Multi-Agent Systems, AAMAS
Computer Supported Cooperative Work in Design, IEEE CSCWD
Instrumentation and Measurement Technonlogy conference, IEEE IMTC
Sensor Mesh and Ad Hoc Communications and Networks, IEEE SECON
Intelligent Data Engineering and Automated Learning, Springer IDEAL

Top 6 journals:
Computer Applications in Power, IEEE
Energy and Buildings, Elsevier
Instrumentation and Measurement, IEEE Transactions on
Autonomous Agents and Multi-Agents Systems, Springer
Machine Learning, Springer
Electrical Power & Energy Systems, Elsevier

End of week 8

This week I have concentrated on collecting as much of the NIALM literature as possible. This has not been a trivial task, as few papers appear in the same journal or conference, however I feel I have a good coverage of the field now. I've been reading the most relevant ones and making notes on the techniques they employ. One promising theme rising out of the most recent papers is rising interest in the low granularity data that could be obtained from smart meters.

My plan for next week is to:

1. Fill in my grid comparing techniques used in each paper
2. Do research methodologies tasks (find top people, conferences, journals, reflect on key conference paper's abstract and intro)
3. Use matlab to apply some techniques from the literature

Post-meeting direction

I'm going to focus on what we can learn from data that can be could be collected from smart meters. Data of granularity this course is rarely used in the literature, as the accuracy of inferences are likely to be lower than for higher granularity.

My to do list:

1. Continue literature search by following references of most recent papers
2. Continue to read most relevant papers
3. Make a grid to compare techniques used in each paper
4. Do research methodologies tasks (find top people, conferences, journals, reflect on key conference paper's abstract and intro)
5. Use matlab to apply some techniques from the literature

In-depth literature search

Following my library skills session this morning, I conducted a more thorough search of the my field of NIALM.

I searched the following databases:

• Web of Science
• Inspec
• Compendex
• LNCS

For the following search terms:

• Non-Intrusive Appliance Load Monitoring (relevant results)
• NIALM (few, very relevant results)
• NILM (non-domestic applications of load monitoring)
• NALM (many irrelevant results)

Among the masses of papers I collected, the following are the most interesting:

• Ruzzelli AG, Nicolas C, Schoofs A, O’Hare GMP. Real-Time Recognition and Profiling of Appliances through a Single Electricity Sensor. In: Sensor Mesh and Ad Hoc Communications and Networks (SECON), 2010 7th Annual IEEE Communications Society Conference on.; 2010:1-9.
• Murata H, Onoda T. Applying Kernel Based Subspace Classification to a Non-intrusive Monitoring for Household Electric Appliances. In: Dorffner G, Bischof H, Hornik K, eds. Artificial Neural Networks - ICANN 2001.Vol 2130. Springer Berlin / Heidelberg; 2001:692-698. Available at: http://dx.doi.org/10.1007/3-540-44668-0_96.
• Benyoucef D, Bier T, Klein P. Planning of energy production and management of energy resources with Smart Meters. In: Advances in Energy Engineering (ICAEE), 2010 International Conference on.; 2010:170-173.
• Pihala H. Non-intrusive appliance load monitoring system based on a modern kWh-meter. Vtt Publications 365. 1998;(May).

I'm planning to start reading through each of these four tomorrow morning, in order of relevance.

Library skills

I completed the ECS library skills for research postgrads this morning. It was a session of just over an hour long, to inform us about the different library services we can use, and the electronic subscriptions the university holds.

Although the session mostly covered material we'd previous been taught, the main benefit was being asked to perform a literature survey using the sources they recommend. Even though the sites and databases were again nothing new, the exercise of investigating the literature without being distracted by reading specific papers was really helpful. I'm going to conduct a similar, but more in-depth search this afternoon.

End of week 7

This week I've spent my time working on two topics. The first has been playing with Gopal's electricity readings and the second has been reading up on machine learning theory.

First I needed to get hold of Gopal's meter readings to the highest granularity possible. I've written a Python script which downloads power readings for a given time period at one second intervals. Using that script, I downloaded a week's worth of data. After plotting the data and having a few conversations with Gopal about his appliance use, we made a few observations:

• The data contained a bunch of erroneous readings, which were represented as 0s in the data.
• He has a time-of-use electrical boiler, which heats up a tank of water at midnight and maintains this temperature by periodically switching the boiler on until 8am.
• His absolute minimum demand is 70 W. We think the 70 W is made up of his Sky box (35 W), a standby TV (10 W) a wireless router (10 W), an extractor fan (5 W) and some other uncountable appliances (10 W).
• However, his average 'always-on' demand is roughly 370 W, his fridge-freezer accounting for the rest. The power drawn by the fridge-freezer seems to vary quite a lot, with a pattern which is does not visibly repeat.
• The energy values given by AlertMe are very similar to the integral of the power demand over time. However, there seems to be a difference of between 0 and 1% between the values that I calculated and the values that AlertMe return.

The other thing I've been up to is reading lots of theory about machine learning. I've read the introduction to probability theory in the Bishop book. I also asked Ruben how to find out more about Gaussian Processes, and he pointed me at chapters 2 and 3 of this book. I'm finding it pretty heavy going, but at least understand what they are now, and am hoping to really get stuck into their applications next week.

A better way to visualise power data

I've found visualising power data on a scatter plot is more useful than on a line graph:



Without the line interpolating between data points, we can clearly see the difference between continuous power demand and single data points which are part of a transient. I've also excluded 0 values. This is because 0 W demand is most likely an error, as no readings exist between 0 W and 70 W readings.