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Dashboard Week 2 – London Energy Consumption

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

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The Challenge

Now we are on Day 2, the challenge of today is to create a dashboard using Smart Meters in London dataset. The whole dataset includes supplementary data including Acorn, holidays and weather.

 

The Data

Firstly, I compared the date range among different datasets and decided to narrow down the data scope to the years 2012 and 2013.

Secondly, when I exploring the data, I found that the hourly consumption dataset is so big, the number of rows for this one table reached about 80 million rows. I spent quite a lot of time improving the workflow performance.

Last but not least, I found the holiday dataset is incorrect, so I went to google to check and fix it.

Anyway, my final Alteryx workflow is like this.

 

The Story

Since today’s dataset is pretty sufficient, I began with playing around with different features. I tried different time granularity, all the weather fields, Acorn groups, holidays, etc. And finally, I decided to only look at three points.

The three points are:

1. Compare 24 hours activities between different Groups

2. Look at the daily differences, including the holidays, in a calendar

3. Find the correlation between energy consumption and weather/temperature

In order to keep the consistency, and make the dashboard easy to understand, I used the hourly consumption for all charts.

The final viz can be found here.

 

What went well

  • Rich dataset leads to tons of interesting angles, easy to derive insights
  • Tried and learnt new tricks

 

What could be better

  • Timeboxing
  • Story design

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