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Dashboard Week | Day 2 – Household Energy Consumption in London

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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For the second day of the hustle week, it was the dataset on the Energy consumption of London via the Smart meter from Kaggle. 

On the initial analysis, the year 2013 had the highest number of smart meters installed. Hence, I chose the same for my dashboard to obtain optimal and well-rounded Insights. 

Approach

I wanted to see how the seasonal variations were impacting the consumption pattern alongside the daily usage. The heat map displays the hourly granularity of energy usage correlating with sunrise and sunset time. 

The other two charts are around how the typical weather or seasonal variations impact energy consumption.

Challenges 

  • My initial efforts to use the entire dataset was challenging when the data was inconsistent across the years, not by pattern but by volume. It was learning to pick the right sample size to represent a trend.

Trivia

 When looking at the consumption pattern, I understand how the Great Smog of London in 1952 has occurred and why thermal plants were kept operational during the coldest period.

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