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.

Thursday, 3 April 2014

Smart Houses with Batteries and Renewable Generation


The preferred method of electricity consumption in the UK is to buy electricity from the grid - through a contract with an electricity retailer - and use it for daily needs such as lighting, heating, cooling, cooking, etc. However, several alternatives to this simple unilateral flow of energy from the grid to devices exist. We will focus on two other types of energy consumption behaviour, enabled by both domestic energy storage and production.

Energy storage consists of storing electricity under an alternative form (potential, chemical, thermal, mechanical, etc.) and, when needed, converting it back to electrical energy. Electricity storage has begun to be applied to houses and buildings, and it shows potential to both reduce customers' energy bills and help bridge the gap between energy demand and supply. Electricity is usually more expensive at peak times, and, generally, is cheaper at night than during the day. Therefore, using batteries to store electricity at night and re-use it during the day can be profitable.

Domestic energy production relies on harvesting energy from natural sources (such as wind, solar radiation, etc.) and using this stream of energy along with energy coming from the grid to meet a home's demands. The UK has installed solar panels on half a million houses so far, and plans to extend this to 10 million by 2020 [1]. With such equipment, households are not only able to produce a portion of the energy they use, but can also directly sell the energy they produce back to the grid through feed-in tariffs.

These two approaches to energy distribution, domestic production and usage can even be combined to create smart houses (see Figure 1), which are powered by incoming electricity from the grid, battery discharge and renewable energy. We can observe that widespread implementation of such strategies smoothes the electricity peak demand, allowing energy producers to more accurately predict the overall needs of the grid.

Figure 1: Schematic of a house equipped with both a solar panel and a battery.

Proof of concept

At Cambridge Energy Data Lab, we prefer crunching actual data to help us make real energy predictions. Therefore, we analysed the electricity consumption of approximately 500 houses, all equipped with lithium-ion batteries, and approximately 40% also equipped with solar panels.

We first focused on the group of houses equipped with batteries only and analysed their daily energy usage. The aggregated (averaged over all the users, for the winter period) results are displayed in Figure 2.

Figure 2: Averaged daily electricity usage for accommodations equipped with battery but no solar panel.

We can clearly see that a sizeable fraction of the energy required during the day (when the electricity rates are expensive) is shifted to the night through the charging of the battery. This stored energy is then released during the day. When electricity becomes cheaper later in the evening, the battery starts to charge anew.

The same analysis can be carried out for the group of users with solar energy generation. The results are presented in Figure 3.

Figure 3: Averaged daily electricity usage for houses equipped with battery and solar panels.

The same observations are made for this group of users: the daytime energy demand is partly moved to nighttime. Moreover, during the day, solar power is produced and allows for less intensive usage of the battery. When a surplus of energy is present, it is sold to the grid and generates income.

When looking at the data for a single day (see Figure 4), we realise that the combination of both solar power generation and energy storage with a battery is very effective at minimising electricity purchase from the grid, especially during the day when it is the most expensive.

Figure 4: Single day analysis: the energy bought from the grid is minimised during the day.

Conclusion and further analysis

This preliminary analysis shows the potential of individual electricity generation and storage. The electricity usage in a house can be distributed along different streams and optimised to reduce the overall cost for the customer. But these installations are very costly too. How long would it take you to reimburse such an investment? We plan to analyse this in the future, so stay tuned!


Wednesday, 2 April 2014

Energy Surplus Trends from Domestic UK Solar Panels in October 2013 to January 2014

According to the statistics provided by the Department of Energy and Climate Change, around 1,900 solar schemes, Feed-In-Tariff (FiT) for solar panels installation, have been installed every week during the past year in the UK. By 5 January, about half million solar schemes had been installed in total.1,2 The solar energy revolution has started, but how much energy can actually be produced using solar panels?

At our lab, we analyzed the energy surplus produced from 1 October 2013 to 31 January 2014, and here we report some basic statistics about this selection of customers.

In these 4 months, our customers had an average energy surplus of approximately 827 kWh, which is similar to the average monthly energy consumption of an American house.3 The lowest surplus obtained by a customer was approximately 340 kWh, while the highest was approximately 1483 kWh.

We recorded the highest peaks of the energy surplus between November and December, although two others significant peaks were recorded in the first half of October and at the beginning of January.


  1. Solar panels on half a million UK buildings, figures suggest, Jessica Shankleman for BusinessGreen, part of the Guardian Environment Network
  2. Weekly solar PV installation and capacity based on registration date
  3. U.S. Energy Information Administration website