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New figures from Clir Renewables, the leading cloud-based AI platform that provides asset managers and owners with tools to maximize annual energy production, surpassed a total of 6 GW of assets on its platform in 2019, a three-fold increase from 2 GW at the end of 2018. With a number of asset owners currently progressing their portfolios through Clir’s full onboarding process following successful trial periods, this growth shows no sign of slowing.
Across the onshore and offshore wind industry, unexpected turbine downtime and underperformance can see energy production – and therefore revenue – significantly lower than forecast. This can have a substantial effect on the financing of wind projects, particularly as large investors move away from fossil fuels and towards renewable energy. As such, improving turbine performance to maximize energy yield is vital to ensuring these investments remain profitable.
Clir uses artificial intelligence to analyze wind turbine data. The software identifies causes of underperformance, from blade icing to suboptimal derating plans, and provides asset owners and operators with strategies to improve performance and thereby increase annual energy production by up to 5%.
Identifying the true causes of underperformance from turbine data can be extremely difficult using typical methods of analysis. By using artificial intelligence to analyze turbine data, however, it is possible to generate a baseline of performance and recognize patterns that indicate not only when the turbine is performing sub-optimally, but why. This gives owners and operators the complete understanding of their asset necessary to take action on underperformance.
“As investors in renewables increasingly focus on asset performance and revenue certainty, we are able to use artificial intelligence to support wind farm owners in developing a complete understanding of their asset necessary to fix faults, maximize asset lifetime, and optimize for both performance and profit,” said Gareth Brown, CEO, Clir.
“Much of the information that owners need to fully optimize their assets is difficult to parse out from raw wind farm data. Typical analysis cannot provide an accurate understanding of whether energy is being lost due to wind resource or whether energy is lost as a result of asset underperformance. But by using AI, Clir can make those distinctions clear to owners, allowing them take informed actions to improve performance.
“2019 saw our biggest period of growth to date, hitting a new milestone of 6 GW, which we see is a clear indicator of the demand for wind investors to better understand their projects as the markets continue to evolve.”