The smart meter rollout: current status

We could write a very long blog post answering questions like "What are smart meters?", "Why do we need smart meters?", "Who is installing smart meters?", or even "Can smart meters read my mind?"... But instead, we'll stick to the data available out there, plot it, and try to analyse it! If you are interested in knowing more about smart meters and how you can benefit from them, we advise you to read the nice and simple article about smart meters on uSwitch.co.uk, or our own blog post.

A smart meter looks like this:

Figure 1: A smart meter!

The smart meter rollout timeline

In 2007, the UK government started to investigate the possibility of a smart-meters rollout. In 2009, it was agreed to proceed with the rollout with a target of replacing every single traditional meter with its smart version by 2020. An intermediate target is to have 20 million meters fitted between 2016 and 2018. The peak of smart meter installation should happen in 2019... a year before the target. Let's check where we are now.

Current status of the rollout

Thanks to the great data portal of the UK government, we can access some numbers about the smart meter rollout. The number of domestic meters by type and quarter is represented in figure 2.

Figure 2: Number of domestic gas and electricity meters by meter type and quarter. Click on "Traditional Meters" to realise how far we are from the 2020 target. No reason to panic though... 5 years to go.

First of all, some jargon clarification. "Smart Meters" is the official term to design licensed meters as defined by the regulatory organism OFGEM. "Smart-Type meters" corresponds to meters installed by utility companies which have some similarities with smart meters (they can store real-time consumption data, be accessed remotely...) but don't fully comply with the current regulation. Therefore, they will have to be replaced by official smart meters by the end of 2020.  We now understand that smart meter is a very precise term and being able to display electricity consumption does not necessarily qualify your device to fit in the "smart meters" category.

By the end of 2014, 500 thousand smart meters had been installed which corresponds to a tiny percent of the totality of gas and electricity meters. The beginning of the massive rollout should however happen in 2015 which should be an exciting year for the smart meter rollout and therefore for electricity data-analysis.

References:

http://www.energy-uk.org.uk/policy/smart-meters.html
http://www.smartenergygb.org/national-rollout/how-its-happening
http://www.uswitch.com/gas-electricity/guides/smart-meters-explained/
http://www.energysavingtrust.org.uk/domestic/content/smart-meters
https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/387725/Smart_Meter_Statistics_Data_Quarter_3_2014.xlsx
http://en.wikipedia.org/wiki/Smart_meter#United_Kingdom

How Do You Use Electricity ?

Collecting data with smart-meters

Smart-meters, through their ability to communicate data instantly, are re-shaping the electricity market landscape. Indeed, these new-generation meters collect and transmit instantaneous electricity consumption data, which can then be used by various actors ranging from the user (e.g. to monitor its own usage) to the supplier (e.g. to forecast energy demand) via independent companies (like Cambridge Energy Data Lab) which help make more sense of this data.

In this short study, we will focus on identifying generic behaviours of electricity consumption within a dataset of more than 400 users for the February-March 2014 period. Because the raw dataset is impossible to interpret, we will perform what is usually referred to as a "model reduction."


Principal component analysis

The first step in the analysis is to perform a model reduction to define several types of days. Amongst all the unique day time-series, we select few thousands (8000 days exactly, out of the 60 days x 400 users = 24000 total days available) in order to perform a Principal Component Analysis.  PCA is a linear algebra method used in order to find directions of largest variance in a dataset composed of several samples of a given variable. See figure 1 for a visual example.

Figure 1: PCA, 2-dimensional example. PCA finds the orthogonal directions which maximise the variance of the samples. 

After having performed a PCA, we can order the different samples along the first principal component (PC1 in Figure 1). We perform a PCA on the dataset composed of the 8000 different days of electricity consumption and order the different days along the first principal component. The result is presented in Figure 2.

Figure 2: PCA performed on a dataset of 8000 days of electricity consumption. The days are ordered with respect to their coordinate along the first principal component.

We notice that the days are now ordered with respect to a relevant criterion since we can detect a continuous evolution from users consuming electricity during the day and in the evening (top of Figure 2) to users who mostly use electricity in the evening and at night (bottom of Figure 2). From this observation, we can therefore define different types of days.

We then simplified the full dataset thanks to this criterion, creating around 10 different "types of days." It is therefore possible to simplify the 2-month time-series by attributing a value to each day corresponding to its type. This is represented in the left panel of Figure 3.

Figure 3: Left panel: unordered "type of day" time-series. Right panel: ordered "type of day" time-series obtained by ordering along the first principal component. We notice an evolution from users who mostly use electricity during the day (top) to users who mostly use electricity at night (bottom).

By re-applying the concept of first principal component ordering, we can re-order the simplified "type of day" time-series. This is presented in the right hand side panel of Figure 3. This time, more than ordering the time-series of the days, we manage to order the users. Each separate user can therefore be attributed to a category, depending not only on the type of daily consumption, but also on the longer time-scale (weekly, monthly) behaviour. Indeed, at the top of the right side panel are represented the "type of day" time-series for the users consuming electricity mostly during the day and the evenings, whereas the bottom part of this colour plot is associated with users consuming electricity mostly at night time. We can also notice on this figure a longer time-scale behaviour ordering, and the signature of the week-ends where people tend to stay awake (and use more energy) later at night.

Conclusion

The large amount of data collected by smart-meters can only been visualised and interpreted by using advanced mathematical tools, PCA being one of them. This method allowed us to successfully define different types of days in terms of electricity usage and therefore simplify the complete users' electricity time-series. From this model reduction, another PCA was then performed to directly order the users, therefore gaining insight about the different types of electricity consumption behaviour present in the dataset.