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.

Monday, 31 March 2014

From energy consumption data to energy profiles

We are all using electricity, non-stop, 24 hours a day. Electricity is our constant companion, but hardly anyone thinks about electricity when we turn on a light or watch the news on TV. Our utility bill is only some abstract amount of electricity we used over a long period of time, so we pay and forget about it.
At Cambridge Energy Data Lab, we think there is much more value in the details of how you use electricity. We believe that if you know and understand your own electricity usage better, you can save money and energy. Our aim is to create insights from your day-to-day electricity consumption to find the best price plan for you, and to convey this knowledge to help you adjust your habits and take control of your electricity usage.

Electricity consumption is not necessarily very regular and depends on many different factors. This is the electricity consumption of a household over a period of days:
The raw energy usage of an exemplary household.  

We can see immediately how the energy usage of this household is extremely irregular, so will have to do some further data analysis to provide the insights we are aiming for. Let's try to create an aggregated energy profile for this household. Using time-series analysis, we first decompose the original data into a periodic day-to-day component, a trend component, and a remainder which cannot be explained by the periodic and trend component:

The decomposition of the raw electricity usage data of a particularly low-usage household. The first panel shows the original data set which we decompose into 3 separate components: a periodic day-to-day element (called seasonal in timeseries analysis), the trend component indicating a smooth overall trend, and the remainder which is the partial data which cannot be explained by the other components.
This decomposition based on a daily interval is just the first step. Let's look at the periodic day-to-day component and decompose it a second time on an hourly frequency interval:

The second decomposition of the periodic component from the previous decomposition. We now have an periodic element with a hourly frequency. The remainder has been omitted. 

From the seemingly irregular raw data, we arrive at an aggregated energy profile which shows a clear trend of high electricity usage between the morning and evening hours. We can also see the periodic element of appliances, such as a refrigerator, in the seasonal component. This is just the first step, however, and much more sophisticated analysis is still to come. Nevertheless, it demonstrates that even your day-to-day electricity usage, though it doesn't look like much, is full of valuable insights.

Our goal is to develop methods and tools to make your electricity consumption data accessible to you! 

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