Budde says the data generated through smart grids can be combined with data coming from the weather forecast, TV networks, city cultural event planners and so on as they all affect the electricity networks.
"If you combine all of that data and analyse it then you know if there is a big event and everybody is going to be watching the television tonight, so you know what to do with your network. If you know there's a storm coming, you know that has an effect on use of the electricity, as people stay home and use heaters to stay warm," he says.
"Also, the mobile companies can say to the electricity companies 'hey, there's a big event in town so expect 100,000 people using their mobile phone at once and make sure you network in the city is able to handle that capacity."
Smart appliances will be able to connect with the smart grid to schedule power-consuming tasks during off peak periods of the day and save on energy. The customer would instruct the energy retailer when to automatically turn on their washing machine, for example.
The NICTA team is using machine learning and predictive analytics to anticipate when a water pipe in Sydney is about to break. Water utility companies can only afford to inspect about 1 per cent of their pipes, and there is 20,000 kilometres of underground pipes in Sydney alone. Busted water pipes overall cost the Australian economy more than $1 billion a year.
"If they break, the people who own the pipes and run them are responsible for the damage. So if you flood a basement of a building and block a road and that stops some deliveries happening, the companies affected will come and get you and will want compensation. If a really big one in the middle of a city breaks, it could cost $5 million in compensation in economic damage," says Economou.
Some 326,571 pipes in Sydney were analysed, with 75,000 failure records. The team looked at the correlations between the age of the pipe, location, material and water pressure. In Wollongong, for example, location/geology and material had a strong correlation, showing that older pipes don't necessarily break earlier.
The team analysed the first nine years of 10 years worth of historical data to predict what would happen to the pipes in the 10th year. They then looked at the data from the 10th year to see if it matched their predicted result and it almost did completely.
Carly Perry, business manager in infrastructure, transport and logistics at NICTA, says the organisation compared its analysis with that of water utility companies and found it could predict twice the amount of pipe breakages using the same maintenance budget.
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