We are Cambridge Energy Data Lab, a smart energy startup based in Cambridge, UK.
This blog, named "Cambridge Energy Data Analysis", aims to incrementally unveil our big data analysis and technologies to the world. We are a group of young geeks: computer scientists, data scientists, and serial entrepreneurs, having a passion for smart energy and sustainable world.

Wednesday 18 February 2015

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, 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.


Friday 13 February 2015

Processing multi-dimensional data visually

In an earlier post I discussed the challenges we face currently at CEDL when we look at Big Data ( This has been complex already, but we love new challenges here at CEDL. So let’s talk about multi-dimensionality.

If we presume that our data is arranged in a table like this:


then aspects of big data refer loosely to the number of rows and multi-dimensionality of the data refers to the number of columns. Basically, we do not only have a lot of data (rows) but it is also complex due to the high number of features (columns).

Understandably, it is very challenging to extract information from such complex data in particular when we do not know what we are looking for. As part of the data exploration a data scientist will look for patterns or clusters that might tell us more about the processes which shape the data.

Of course, a data scientist wants to use the best tools available to find patterns and clusters in the data and as it turns out the most powerful machine for pattern detection is the visual cortex! The brain is your very personal supercomputer. The challenge in utilising the brain for detecting patterns in multidimensional data sets does not, thankfully, come down to brain surgery. Nevertheless, a problem still remains: how to interface the visual cortex with the data set? The only and best working interface are of course the eyes. All what is required is to transform the data set into a representation suitable for the eyes -> visual cortex interface. You might wonder why this sounds rather like an engineering problem than the typical task of a data scientist.  Unfortunately, the role of a data scientist is commonly misunderstood. In fact, with today’s challenges the task is not so much about calculating statistics but to engineer a way to access and consume data.
Usually, this happens in the form of charts and plots and it is up to the data scientist to find a suitable data representation for the problem at hand:  to explore data, find answers, and to communicate them.

For example, a fantastic way to represent multidimensional data are Parallel Coordinates [1] if you want to utilise the pattern recognition abilities of the brain’s visual cortex.

In a chart with parallel coordinates each column of the table is a vertical axis and each row becomes a line in the chart. Here follows an example:

This type of chart works with both discrete and continuous data. Additionally, colour and line types can be used to add some additional context to the data. Obviously, this chart is very simple but it can help us understand how parallel coordinates work. For example, looking at the location axis we can note that we have three records with location London and three with location Cambridge while looking at the axis data we note that we have two records for each day.

This chart shows you parallel coordinates in full action:

The chart shows the visualization of the mtcars dataset [2]. The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of car design and performance for 32 cars (1973–74 models).
A way to explore data in parallel coordinates is called “brushing”: Simply select a range over one or multiple axis and explore how the data segregates.
For example compare the models with better fuel economy versus models with less miles per gallon (mpg):
Whereas the cars with low fuel economy don’t seem to show any specific segregation, the cars with good fuel economy are light cars with 4 cylinders and small displacement.

Tuesday 10 February 2015

Renewable energy in Europe: how far are we from the targets?

In 2009, the European Union set mandatory targets for renewable energy use that every member state has to reach by the year 2020. In this post we will analyse the progress of each member state using the latest estimates released by Eurostat.

Shares of renewable energy in 2012

In the figure below we have the shares of gross final renewable energy consumption for each member state and how far the states are from their target: Here we note that Sweden, Estonia and Bulgaria already reached their targets while Malta Luxembourg and the UK have the lowest shares of renewable energy in gross final energy consumption. Also, Norway is the country with the highest share of renewable energy. Netherland, France and the UK are the countries furthest from their targets.

Increase since 2006

In the following chart we compare the increase of shares from 2006 to 2012 of each country: From this chart we note that all the member states increased their share of renewable energy since 2006. Another interesting fact we note here is that the three states with the highest increases are, in order, Malta, the UK and Belgium, which are also some of the countries furthest from the achievement of their targets.

Evolution of the shares from 2004 to 2012

In this figure we compare the trend of the shares of renewable energy among the biggest European countries excluding the Scandinavian ones: We can observe that Italy and the UK had the fastest growth of renewable energy shares, but while the UK share has never been comparable to the ones of the other countries, Italy was able to overtake France and Germany in 2011. We can also see that the German share had the slowest growth and that Spain is the country with the highest share since 2009.