Machine learning and the connected home: useful data or meaningless noise?

Sometimes we get lucky with the speakers for our weekly energy seminars. Last May we organised a workshop to build up a community of academics and industry to look into the Connected Home. Today’s seminar was one of the attendees on the day Oliver Parson of Centrica Connected Home. His talk, and this blog, explored how machine learning can extract value from data from connected devices. You can also download the slides from his talk [PDF].

Smart meters will be installed in 26 million UK homes over the next few years in an effort to achieve the country’s carbon emission reduction targets. Such smart meters will conform to the SMETS2 specification, which allows customers to chose whether to upload daily or half-hourly data to their supplier over the cellular network for billing purposes.

At Centrica Connected Home, we developed the My Energy dashboard for British Gas. This dashboard aims to not only visualise energy consumption, but also to extract meaningful insight from the consumption data. The dashboard offers a comparison of the customer’s consumption against similar homes, and also a monthly breakdown of their consumption into six categories; heating, hot water, lighting, entertainment, cooking and other appliances.

The centrica connected home

For the similar homes comparison, we rephrased this problem as the following question: “can we predict daily consumption given only the customer’s location and answers to a short survey?” We then built an algorithm to answer this question, and optimised the accuracy of this prediction given the huge dataset of all our customers’ actual consumptions. However, we realised that it was also important to balance single-day accuracy against the day-to-day stability of a single customer’s predicted consumption.

For the energy breakdown, we consider the problem again as a prediction problem, in which individual blocks of energy are detected from half hourly data and assigned to one of the six categories based on a range of features, such as magnitude, duration and time of day. We then optimised the accuracy of the algorithm using data collected from the Household Electricity Survey, which measured the consumption of individual appliances in addition to the total household’s consumption.

shutterstock_143276602_connected_homes_croppedIn addition to My Energy, Connected Home is probably best known for developing the Hive ecosystem of products, including Active Heating, Lighting, Motion Sensors, Door & Window Sensors, Smart Plugs and Boiler IQ. I’ve chosen to focus on Hive Active Heating in the rest of this talk, given that it’s the product that I’ve spent most time working on.

Hive Active Heating is a connected thermostat that allows customers to control their heating from their phone. However, Hive doesn’t instrument the boiler directly, but instead sends control signals to the boiler based on the ambient temperature of the home and the customer’s desired temperature. We’ve recently been experimenting with the possibility of detecting boiler failure from this limited set of features. Such a failure might consist of Hive asking the boiler to heat the home, followed by a decrease in the ambient temperature (rather than the expected increase in temperature). While this algorithm is still in its early stages of development, it illustrates a clear possibility to turn a connected product into a truly smart device.

In conclusion, I believe that smart meters offer huge potential to give customers insight into their energy consumption. Furthermore, I see real potential in the Internet of Things market, not only in connecting everyday appliances to the Internet, but also by enabling the insight and automated control which transforms them into smart appliances.

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