Big Data Systems and AI to Cut Electricity Bills
How big data and artificial intelligence are to be used to cut down consumer electricity bills and find and repair faults in electrical grids.
University of Queensland, Australia
Artificial intelligence is going to be used to cut power bills in thousands of homes across Queensland, Australia. The University of Queensland, an energy start-up called Redback Technologies and other partners are collaborating on an experimental project that will involve at least 200,000 properties in the initial trial.
At the heart of the initiative is a big data platform that the university is building. It will use information from participating homes to control energy usage and cut costs. The data will be submitted to a secure cloud via smart meters in each home.
"Consumers will be empowered to take control over their household power generation, storage and consumption and are expected to see big reductions in their power bills," said Professor Xiaofang Zhou from the University of Queensland.
Individual Energy Use
When the data is collected the system will provide insights about each household's usage, and using machine learning will be able to make predictions and suggestions about how consumers can make the most effective use of their energy. Residents will get immediate feedback on how much energy they are consuming for each appliance.
According to the team behind the project, it’s the largest of its kind ever carried out in Australia and will also help the country's electricity network transition to a more intelligence-based system.
If the project is successful and commercially viable the aim is to roll it out country-wide.
Minimizing Grid Failures
This is not the first time that artificial intelligence is being deployed to improve power systems. In the United States, a project by the Department of Energy's SLAC National Accelerator Laboratory is going to combine artificial intelligence with huge volumes of data and industry experience from a dozen partners to minimize electrical grid failures. The goal is to identify places in the grid that are vulnerable to disruption, reinforce them in advance before they become a problem and recover faster when failures occur.
The system will use machine learning, where computers teach themselves about how a system behaves based on the large amount of data they receive. Meanwhile, artificial intelligence will apply the knowledge the machines have accrued to solve problems.
"This project will be the first of its kind to use artificial intelligence and machine learning to improve the resilience of the grid," said Sila Kiliccote, director of SLAC's Grid Integration, Systems and Mobility lab.
If his project is successful it could have a nationwide impact. Those involved said that the tools they develop will either be made available commercially or open source.
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