Challenge
2

ML - Anomaly Detection in Smart Meter Data

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


Challenge Description

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.

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.

All attendees, sponsors, partners, volunteers and staff at our hackathon are required to agree with the Hack Code of Conduct. Organisers will enforce this code throughout the event. We expect cooperation from all participants to ensure a safe environment for everybody. For more details on how the event is run, see the Guidelines on our wiki.

Creative Commons LicenceThe contents of this website, unless otherwise stated, are licensed under a Creative Commons Attribution 4.0 International License.


Updated 15:45 27.07.2020 / Maintained by portia90

  • 15:45 27.07.2020 / portia90 / star
  • 15:45 27.07.2020 / portia90 / create