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.