June EnergyIncrease the quality of the
energy consumption data
in an automated way

Explore the case

About the client

June Energy is a Belgian start-up focussing on the consumer energy market. Their main goal is to make consumers pay as little as possible for their energy by switching them to the cheapest energy supplier based on their unique energy consumption profile.

June Energy

The problem

To keep track of their customers’ energy consumption, June Energy provides ‘smart cameras’ which are installed on top of electricity and gas meters, converting them into ‘smart connected meters’. These cameras capture analog meter readings and send them to the June Energy cloud for processing. Those images are then converted into digits, representing energy consumption data and thus providing insight into energy usage. However, the image quality might be low, making the image unreadable. There are multiple reasons for low quality images:

  • Dust in front of the camera.
  • Too little or too much light influx.
  • Misalignment of the camera frame.
  • Spiders in front of the camera.

The low quality of the images had a significantly impact on the quality of the energy consumption data, causing the need for manual corrections afterwards.

Sentigrate was consulted to increase the quality of the energy consumption data in an automated way, with the goal of reducing human interventions.

The solution

The solution Sentigrate worked out consisted of 3 steps:

  1. Predicting the energy consumption at the moment an image is taken. The prediction takes into account the customer’s usage pattern, historical energy consumption data and the image itself.
  2. Comparing the prediction to the raw conversion of the image into digits. Therefore, a custom distance metric (based on the Levenshtein distance) was developed.
  3. Based on the difference found in step two, the energy consumption data is corrected to be closer to the predicted value of step one.


steps to increased the accuracy


reduction in manual corrections

Increase in accuracy

This three-step system increased the accuracy of getting the right energy consumption data significantly and caused a reduction in manual corrections by more than 98%.

In the future June Energy will be able to use the prediction algorithm of the first step for other use cases. For example, customers might be interested in their energy consumption of the upcoming week.

June Energy explanatory video

Company Facts

Number and facts about the case

Smart Sensor integration
Output of data via app