Using data from electricity meters to predict energy consumption
Learn how a research institution used clustering to optimise heating and energy consumption.
This guidance is part of a wider collection about using artificial intelligence (AI) in the public sector.
AI technique used
- clustering
Objective
A research institution needed to understand which electric appliances were being used in a house at a certain time to optimise heating and energy consumption.
Situation
The research institution did not know when particular electric appliances were being used. This meant they were unable to optimise heating and energy consumption resulting in higher prices and energy waste.
Action
The research institution used non-intrusive load monitoring to gather unlabelled data from electricity meters to see which appliances were being used and when.
They used unsupervised machine learning techniques to convert the unlabelled data into patterns. From this, the research institution could cluster the different types of appliances based on their power consumption patterns.
Impact
The model:
- was able to predict future energy needs of a property
- could help plan when households might use appliances
- enabled smart use of heating - for example turning off heating while the occupier is out and turned on when they are coming home
Related guides
- Understanding artificial intelligence
- Assessing if artificial intelligence is the right solution
- Planning and preparing for artificial intelligence Implementation
- Managing your AI project
- Understanding artificial intelligence ethics and safety
- Examples of real-world artificial intelligence use
- National Cyber Security Centre guidance for assessing intelligent tools for cyber security
- The Data Ethics Framework
- The Technology Code of Practice