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Challenge: Anomaly Detection

Participants are asked to use machine learning in order to identify abnormal consumption from smart meter data.

Challenge Description (#14)

Energy consumption in buildings and industry is often wasted due to user behaviour, human error, and poorly performing equipment. In this context, identifying abnormal consumption power behavior can be an important part of reducing peak energy consumption and changing undesirable user behavior. With the widespread rollouts of smart meters, normal operating consumption can be learned over time and used to identify or flag abnormal consumption. Such information can help indicate to users when their equipment is not operating as normal and can help to change user behavior or to even indicate what the problem appliances may be to implement lasting changes.

This challenge is looking for data scientists to apply their skills to an anomaly detection problem using smart meter data. Ideally, such an algorithm should begin to operate after as little as 3 months and should improve over time. A platform to visualise the anomalies would also be useful. Users can select any type of machine learning algorithms that they wish to in order to detect the anomalies from the data.


A sample including smart meter data can be found on kaggle. Participants are encouraged to find other smart meter data to work with in order to test their algorithms.

Event finished

Joined the team

28.08.2020 10:23 ~ manuel.baez

Event started


27.08.2020 13:04 ~ nikki_bhler

Joined the team

26.08.2020 02:25 ~ xuewang


24.08.2020 15:34 ~ oleg

Joined the team

27.07.2020 15:45 ~ portia90

First post View challenge

27.07.2020 15:45 ~ portia90


Contributed 3 years ago by portia90 for Energy Hackdays 2020
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