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Senseye, a UK start-up, is looking for PV firms to test a military-grade analytics system that could cut operations and maintenance costs by 20%.
The company aims to release the system commercially early next year, hoping to undercut current the price of existing component maintenance forecasting systems, said Alex Hill, one of Senseye’s founders.
The Senseye system will use software adapted from the defence sector to analyse large amounts of data in real time and provide accurate predictions regarding variables such as plant output and component failure rates.
One example might be to combine inverter output readings with data from weather forecasts and currency fluctuations to give plant owners a good idea of how much money they will make from day to day or week to week. Another would be to predict when components might break.
The aerospace and defence industries are heavily dependent on advanced analytics software to make sure components do not fail unexpectedly, Hill explained. As a result, they have developed forecasting systems worth millions of euros.
Senseye is now hoping to make the same systems available to the solar industry, at an affordable price, by spreading the cost across a large number of users. “We founded Senseye at the beginning of this year, to take this technology and commoditise it,” Hill said.
His company, which is supported by a south England regional low-carbon business incubator called Future Solent, is currently targeting the solar, farming and manufacturing sectors for a six-month beta-testing pilot to demonstrate the value of the system.
Although the Senseye system can be used anywhere in the world, the company is restricting the pilot to around half a dozen UK solar companies in case it needs to install additional sensors and electronics in the PV plants being used for the test.
As it is, he is hoping most of the information the system needs, such as inverter outputs and kilowatt-hour production readings, will already be readily on hand to plant owners. Said Hill: “The more data we have the better, but solar already has a lot of data available.”
This data will then be aggregated and analysed to make predictions about component and plant performance. In aerospace, such systems have been shown to cut costs by around a fifth.
The collection and analysis of large field-based data sets, or ‘big data’, forms part of a wider trend called the Internet of Things, where everyday objects are wired up to communications networks so they can be monitored and controlled remotely.
The Internet of Things is already present in the energy sector through the development of smart grid, and elsewhere is credited with cutting maintenance costs by 25% and reducing unplanned outages by up to 50%.
Steve Hilton, an analyst at the Internet of Things consultancy MachNation, said: “Predictive analysis is generally the domain of large enterprises that have access to fairly complex analytics applications from major vendors.
“It would be pretty interesting if you could offer predictive analytics that is financially realistic and simple enough for small and medium businesses to consume.”
And Dr Ingeborg Rocker, vice president of Dassault Systèmes, which specialises in software systems for data analysis and visualisation, said the benefits of applying analytics technology to sectors such as solar were “absolutely clear.”
She said: “To obtain data from sensors and use it for predictions is the way to go. Traffic prediction systems are already doing this.”