Many companies take in and use data which contains information that it is not practical for a human to extract. A company may gather data to use for billing purposes but also possess the capability to learn a great deal about their customer from proper analysis of this data. Extracting this information and the value contained within can allow companies to better understand their customers and their behavior.
In this case-study, we disaggregate overall household energy consumption data into constituent appliance energy requirements. The benefits are threefold - 1) informs households of which appliances are using the most energy 2) personalised feedback to quantify savings for appliance-specific advice and 3) provides capability to build recommender systems to inform households of savings that can be achieved. In this particular case, SFL worked on data from a national European energy provider to expand their offerings.
SFL works to extract as much information as possible from clients’ data. For this European energy provider, we accomplished this by implementing algorithms to disaggregate their data. Disaggregation allows a company to convert a time series of power data into a broken down list of when each individual appliance (whether it is a refrigerator, toaster, or heater) was turned on/off. The algorithms that were used took raw time-series data and trained variants of Hidden Markov Models (HMM) known as combinatorial HMMs. This allowed for a model to be built that separated, per customer, the underlying behaviour of appliances. This increased resolution was always present in the data but SFL’s expertise was able to unlock it.
Maximizing the value of existing data is always important for a company. Knowing more about customer behavior lets companies make more accurate and responsive decisions, and reach out to specific customers in targeted ways. In this case, the data was used to identify faulty devices using too much power and design more targeted programs aimed at reducing energy usage. It can provide further feedback to households to encourage cleaner appliances by stating expected savings and also targeted advertisements for new energy-efficient appliances. This kind of analysis can be taken further, with automated feedback, diagnostics, and repair servicing, that can be provided to the customer for greater visibility into the energy expenditure. On a larger scale, these types of data allows for a more accurate modelling of energy usage across the country and can allow better energy generation and storage optimizations.