Tackling data unknowns is vital for continued growth of offshore wind

Tackling data unknowns is vital for continued growth of offshore wind

Offshore wind can greatly reduce operations and maintenance costs and increase power output with a better understanding of asset performance

Until the offshore wind industry uses its data effectively to quantify the impact of offshore conditions over turbine lifetime, offshore wind will continue to face high operational costs, miss initially modelled power output capacity, and as a consequence suffer increased financing costs.

This is, at least, according to Clir Renewables, the leading provider of digital asset performance technology for wind energy, which supports over 5 GW of assets worldwide. Clir analyzes wind turbine data via artificial intelligence in order to identify causes of underperformance, informing asset owners and operators of strategies they can take to improve performance and thereby increase annual energy production by up to 5%.

The offshore wind industry is credited as the principle way forward for utility-scale renewable energy across many markets, and is set to expand up to 70 GW to account for 9.9% of Europe’s electricity needs by 2030. However, this growth continues to be accompanied by a proliferation of ‘operational unknowns’ that risk this target being missed.

Indeed, as recent financial results from major offshore wind operators have demonstrated, revenues may be pushed downwards by risks that in Clir Renewables’ opinion were predictable. If offshore wind technology is to be effectively capitalized upon in the future, it is important that owners and operators are investing in technologies that provide a more granular understanding of operational performance and feeding back lessons learned.

“Larger turbine designs and the stratified atmosphere found in offshore wind have pushed the demands facing this technology to greater heights than ever before,” commented Gareth Brown, Chief Executive Officer, Clir.  “Clarifying the effect of the harsh offshore conditions on new turbines, however, still remains a challenge for the industry.”

“We know, for example, that capacity expectations have been developed optimistically, as you can’t take underperformance into account if you rely on old design assumptions which don’t cover the performance issues new offshore turbines, such as atmospheric stability that drives wake effects and the blockage effect. Focusing on these unknowns is key to informing financial decisions for future expansion of the industry.”

“Artificial Intelligence, and its proliferation into renewables, is an essential tool to answer many of these questions, but its integration must be based on deep domain expertise applied and built with transparency from all stakeholders.  Only then, can we take a further look at performance, drive new cost efficiencies in operational projects and feedback into new development assets.”

“Ultimately, having a complete picture of asset performance will be absolutely crucial to the continued growth of offshore wind.”

Discover how Clir can support your portfolio, book a demo today.

Image: Unsplash

Clir invests in expanding AI capabilities for under-optimized wind industry, appoints Louisa Thue as CTO

Clir invests in expanding AI capabilities for under-optimized wind industry, appoints Louisa Thue as CTO

Innovative wind asset optimization firm appoints former data and engineering VP to drive the expansion of Clir Renewables’ industry-leading AI capabilities

Clir Renewables, the leading provider of digital asset performance technology for wind energy, has announced the appointment of Louisa Thue as Chief Technical Officer (CTO). Louisa brings her experience in technology, data analytics, and scaling Software-as-a-Service products to the cutting edge of renewable energy optimization, and will drive the expansion of Clir’s cloud-based artificial intelligence capabilities across the global wind energy industry.

As the global wind industry moves into a post-subsidy marketplace, there is increased financial pressure on asset owners to ensure their projects achieve maximum generating potential. However, recent analysis of wind farm output indicates that in the US alone, the under-optimization of wind assets and subsequent downtime results in the loss of enough energy to power more than one million homes each year.

But, project owners across the wind industry are sitting on an incredibly rich – but largely untapped – resource that can address this sub-optimal performance. AI can make asset data work much harder for projects, ultimately driving more cost-efficient operations and increasing power generation.

Commenting on her appointment, Louisa said: “There is a huge opportunity for software and data analytics in renewable energy, and I look forward to bringing my technical experience to Clir as it invests in optimizing energy generation and asset availability across the global wind industry.”

Gareth Brown, CEO, added: “We are delighted to bring Louisa on board. She’s a powerhouse here in the Vancouver software and data analytics industry, whose reputation precedes her. We are incredibly excited about the impact she will have at Clir as she works with our technology group and data science teams to increase the financial performance of renewable energy assets.”

With a clear market opportunity to assist the wind industry in improving its asset performance, Clir has recently closed its Series A funding round, securing a $7.1m investment from ArcTern Ventures. The funding will enable the company to focus on additional development of its AI-based analytics platform and optimize power generation at a global fleetwide level.

Discover how Clir can support your portfolio, book a demo today.

Clir Renewables scoops award win at the European Wind Investment Awards

Clir Renewables scoops award win at the European Wind Investment Awards

Vancouver based software company secures Start-up of the Year title at European awards.

Last Thursday in a London banquet hall filled with industry peers, Clir Renewables was announced as Start-up of the Year at the inaugural European Wind Investment Awards. Hosted by A Word About Wind, the awards celebrated best practice in the European wind industry.

In winning the award, the Vancouver based Software as a Service company was recognized for its growth in 2018. The host noted, “The judges were impressed with the Clir Renewables growth and achievements. An excellent product fulfilling a true market need.”

The Canadian company opened its first European office in July 2018 to support its growth in the industry. At the beginning of 2018, Clir Renewables had 1.5 GW of assets on its system, which grew to 3 GW by the end of 2018.

After collecting the award, Clir Renewables CEO Gareth Brown, said, “It was brilliant to receive recognition from our industry peers for the impact we are having on the market. There is a huge opportunity for us as we go into 2020 to roll out onto 100 GW of assets and drive up the financial returns of wind farms on every continent.”

By achieving Annual Energy Production (AEP) gains for clients, Clir Renewables is not only supporting owners increase project returns, but contributes to the ongoing transition to clean energy sources.

The judging panel included Paul Maile (Eversheds Sutherland), Laura Fleming (Siemens Gamesa), Carol Gould (MUFG Bank), Marie de Graaf (Orsted), Aris Karcanias (FTI Consulting), Manahil Lakhmiri (Engie), John MacAskill (Offshore Wind Consultants), Carla Riberio (DNV GL), Simon Clement-Davies (Agusta & Co.) and Michael Van Der Heijden (Amsterdam Capital Partners).

The Clir Renewables team celebrating the award win on October 31

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Clir Renewables secures C$7.1m Series A

Clir Renewables secures C$7.1m Series A

Clir brings strategic venture partner to accelerate growth.

Vancouver based Clir Renewables, closed their Series A round of C$7.1m this month, led by strategic investor ArcTern Ventures. Clir’s software platform uses machine learning algorithms to categorize and quantify areas of underperformance across wind farms, resulting in recommended actions for improvement, including yaw alignment and blade pitch. Customers realize Annual Energy Production (AEP) gains of up to 5% from Clir’s actionable insights, leading to improved margins. Clir stands out from competing software solutions in this growing market due to its ability to generate actionable optimization recommendations, moving beyond data monitoring and visualization.

Clir entered the financing round in August this year, seeking investment from a group with similar values and outlook on the coming energy transition. The deal, which was closed in mid-September, enables the company to focus on additional development of its AI-based analytics platform and accelerate growth commensurate with their expansion plans.

ArcTern Ventures is a leading North American venture capital firm investing in breakthrough clean technologies. Led by former start-up entrepreneurs, Murray McCaig and Tom Rand, ArcTern invests at an early stage with the capacity to provide follow-on capital to fund companies through to market leadership.

“Clir leapt ahead of large incumbents in this space, it’s impressive,” said Tom Rand, Managing Partner of ArcTern Ventures. “Their world-leading analytics can pull significant amounts of extra energy from existing wind farms, which aligns them nicely with ArcTern’s core mandate: to match profit with big impacts on GHG reduction.”

Gareth Brown, CEO of Clir Renewables, said, “It is brilliant to partner with ArcTern Ventures. They quickly bought into the vision for our platform and company, and are fully aligned with our values which is so critical for us. This latest round of funding has come at an important point in our growth when our subscribing client base has over 10% of the worlds wind turbines by mega-watt (MW) along with a substantial solar fleet. This round gives us the resources to optimize the global renewable energy fleet.”

 

About ArcTern Ventures

ArcTern Ventures is a Toronto-based venture capital firm investing globally in breakthrough clean technology growth companies addressing climate change and sustainability. ArcTern was founded by Murray McCaig and Tom Rand around the belief that the accelerating transition to a greener economy will disrupt all industries and present a multi-trillion dollar opportunity for both outsized financial returns and positive environmental impact. For more information, please visit www.arcternventures.com.

Discover how Clir can support your portfolio, book a demo today.

Teaching the machines to work for us

Teaching the machines to work for us

Artificial intelligence and machine learning are widely used to analyze vast amounts of data

Artificial intelligence (AI) as a concept has existed since the early 1900s*, but it was often portrayed as a threat to humanity in fictional stories like Mary Shelley’s Frankenstein, the creation turning on its creator. However, a better understanding of artificial intelligence has evolved over the decades. In recent years many use the term along with machine learning to describe developments in the capabilities of software programs and machinery.

What is the difference between artificial intelligence and machine learning? Dr. Shane Butler, Principal Data Scientist at Clir Renewables, says, “At Clir Renewables we approach artificial intelligence as the idea of building a system to do what a human would do, while machine learning provides the statistical methods and tools to automatically learn the relationships in the data, enabling automated decision making – without being explicitly programmed to do so.”

Artificial intelligence has great potential within the wind industry. Large amounts of data are generated in all stages of a wind farm’s life cycle, from feasibility studies through to operations. Manually working with the data is a laborious task, consuming lots of time, and as a result, money. Using AI to analyze, understand, and predict farm behavior is becoming a necessity, given the scale of the wind industry and the number of turbines to be analyzed. AI offers the potential for reduced costs and increases in AEP. At Clir Renewables, we create artificially intelligent tools specifically for the wind industry by combining domain expertise, machine learning experience, data engineering, and software development. When machine learning is robustly tested, users of artificial intelligence tools have confidence in the outcome, and there is a significantly reduced need for human intervention. 

Clir Renewables chooses to use artificial intelligence on its platform to give owners and operators a robust analytical and reporting solution and enable us to scale and apply our industry-leading analytics to thousands of wind turbines. With a software solution powered by AI, data analysis is much quicker, enabling a varied range of analytics to run on the data. All of this analysis produces actionable insights which owners and operators act on to get optimal performance from their assets.

When a cloud-based software solution is used to analyze your wind farm or portfolio, you create a digital blueprint of its past and expected behavior. Clir Renewables’ vision is to create a ‘Digital Wind Farm’. This is a catch-all term used to describe a range of machine learning-based predictive modeling capabilities, which compute the expected value of a multitude of signals at each wind turbine. By developing these capabilities, observed behavior can be continuously compared with the expected behavior, enabling opportunities to identify when a turbine deviates from its expected behavior automatically. Highlighting deviations from expected behavior promptly has a variety of benefits to owners and operators, including preventing failures, optimizing maintenance plans, and identifying areas for increased performance. 

The key to creating artificially intelligent systems is to have an extremely well-defined business problem with deep domain understanding. Once defined, conduct research and development for the model, train and deploy the model, and monitor and debug the model. The value and opportunity in apply artificial intelligence in the wind industry comes from combining both domain expertise and machine learning capabilities.

The figure below illustrates an example output of one of our condition monitoring algorithms – illustrating a deviation between the predicted, fault-free, component temperature, using our trained ML models, and the observed component temperature. The data illustrated spans several weeks. The color illustrates the magnitude of the deviation between predicted and observed component temperature, with WTG 4 operating some 7 degrees hotter than expected – indicating a potential component/system health issue. Using the predictions generated by our machine learning models, our AI-solutions then automatically identify abnormal component or system behavior and flagging any issues identified to the user for review, enabling opportunities to identify and address potential turbine health issues at the incipient stage, and avoid unplanned downtime – while maximizing the utility of maintenance resources.

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