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.