Clir Renewables secured a C$1.9m investment following discussions with multiple investors.
Canadian renewable energy company, Clir Renewables, has successfully closed an investment round, securing C$1.9m. The SaaS company has developed a renewable energy AI platform that helps wind farm owners and operators to maximize production and provides clarity on performance risk for all interested stakeholders.
Gareth Brown, Clir Renewables CEO, says, “It’s exciting times here at Clir. We secured this bridge financing to put us in a sound financial position to continue to grow the company globally and develop our domain-specific AI. It’ll allow us to continue to lower the cost of renewable energy and give us time to find the right Series A investor later this year. It’s fantastic to see so many of the previous investors reinvesting in the company and bringing in impact and renewable industry expertise investors from North America and Europe will align a lot of expertise with the company to facilitate a massive global impact. With a bit of luck, we’ll have over 10% of the world’s wind farm owners paying to use our platform by the end of June. We need to remain humble and focus on execution to drive our industry forward to lower cost of energy”.
Clir Renewables was previously awarded funding through Sustainable Development Technology Canada (SDTC), a Canadian government support for entrepreneurs accelerating the development and deployment of globally competitive clean technology solutions. The company also secured C$2.1m in a seed-stage financing round in 2018. This latest investment enables continued product development, strengthening existing features, and releasing new feature from the product roadmap.
Mike Winterfield, Founder and Managing Partner of Active Impact Investments, commented, “We have been watching the success of the Clir Renewables team for over two years and are thrilled to get an opportunity to support them in accelerating their global expansion. Climate change requires an urgent shift from burning fossil fuels, and the insights provided by Clir’s software will continue to drive the costs of renewable energy down so it becomes the obvious choice in all markets.”
As Clir Renewables gains more market traction, it is considering sourcing larger investors to assist in capitalizing on this traction and increased interest in the software.
Replacing components in a wind turbine is a costly procedure, especially if required urgently; however, it can be avoided or planned for by on-going monitoring of temperatures.
Canadian optimization software company, Clir Renewables, has released its latest AI feature. The Clir AI platform has evolved to learn how to identify anomalies in component temperatures to detect failure at an earlier stage.
budgets for wind farms account for the majority of associated OPEX. These planned budgets can be shattered if unexpected repair is required due to a component failure. Increased expenditure is not the only cost involved with unexpected failures. When a failure occurs, the turbine can be out of operation anywhere from a few days to a few weeks, dependent on sourcing replacement parts or required machinery in a quick timeframe.
The question is, can you predict and prevent component failure? The answer is yes. Clir AI can learn temperature behaviour in the context of the real world operational environment anomalies or trends that could be utilized to identify when a . Once identified this information allows owners and operators to assess components for signs of degradation which if ignored could lead to catastrophic failure.
Clir AI can remove some of the unknowns around unexpected failures by creating actions for the owner or operator to investigate the turbine further. The challenge for wind farm owners is the amount of data and its context. Simple peer to peer trending or other limited algorithms have shown time and time in our industry to frequently lead to false positives that cost owners money and impact confidence and trust in data analytics platforms.
This type of detection is difficult; turbine components heat up and cool down in different ways. Inconsistent measurement instruments between components and temperature are often driven by the conditions leading to a moment in time rather than specific live conditions. Clir AI puts the data in context and takes into account a variety of factors including, but not limited to, service information, . Based on all of this information, Clir AI learns for the turbine. If the temperature varies outside the probabilistic range, the system creates events and actions on the system. It reports multiple grades of severity, based on how much the trend deviates from the expected behaviour and learned failure models in the turbine.
As an independent system, Clir seamlessly integrates this detector with its other features, providing a multitude of information and actionable insights in one place.
“We really wanted to focus on building detection that has limited false positives, so the tool isn’t wasting peoples’ time while maximizing the benefit of early fault detection,” says Clir CEO Gareth Brown. “The approach maximizes the use of the data to drive improved performance, and crucially it can be scaled across all turbine technologies and as components are upgraded or replaced. It’s exciting to see when we take deep domain expertise and apply the latest and greatest AI techniques what we achieve”.
Image: Clir Renewables
Industry veteran, Ian Irvine, has joined the board of renewables software startup, Clir Renewables.
Clir Renewables is delighted to announce the appointment of Ian Irvine to its Board of Directors. Ian has over three decades of experience in the renewable energy industry, beginning his career with a wind turbine design company in the mid-80s before moving to ScottishPower Technology in 1990 and most recently founding award-winning renewable energy consultancy SgurrEnergy. His experience will be invaluable in ensuring the continued growth of the business.
“I am delighted to be joining the board of Clir Renewables. I have always had an affinity with innovative and ambitious renewable energy enterprises and look forward to working with the global team to help customers take advantage of Clir Renewables software offering,” said Ian Irvine.
Throughout his career, Ian has shown his desire to push the industry forward. With ScottishPower Technology Ian developed a team focused on producing accurate, long-term annual energy production (AEP) predictions, selecting wind turbine technology suitable for its deployment location and holding turbine suppliers to account on contractual agreements which were not conventional approaches at the time. Following ScottishPower’s divestment of its business, Ian and one of his engineering colleagues decided to start a renewable energy engineering consultancy focusing on accurate analysis, reporting and effective project delivery.
Converting his apartment’s attic into an office, Ian and his business partner founded SgurrEnergy in 2002. The business grew quickly, and by 2008 the head office in Glasgow had 80 staff with international offices in China, India, Ireland, and Canada and by 2017, when Ian exited SugrrEnergy, the global staff complement exceeded 300. Ian was involved in many industry firsts, the first internationally funded Chinese wind farm and the first project financed offshore wind farm. He also worked on some of the world’s largest projects, such as Europe’s largest wind farm in the 1990s, the 322 MW Whitelee Wind Farm (now 539 MW), attributes of vision, ambition and scale that will be invaluable to Clir Renewables going forward.
Ian’s passion for driving the industry forward led to the development of a LiDAR (Light Detection and Ranging) unit to remotely assess wind speed and direction. SgurrEnergy launched its LiDAR, Galion, at the EWEA conference in 2008. The development continued following an investment agreement with Wood Group in 2010, whereby a team under Ian’s instruction developed a service offering to optimize wind farm production. Ian continually encouraged his staff to identify and exploit opportunities to help the industry progress before exiting the business in 2017.
“Having worked closely with Ian in the past, it made sense to ask him to join Clir Renewables on our journey. His knowledge of renewable energy is vast, and his business skills have seen him successfully operate in a variety of countries, and we plan to capitalize on that,” said Gareth Brown, CEO of Clir Renewables.
Ian joins Jenny Yang on the Clir Renewables board. Jenny, a serial entrepreneur, and angel investor joined the board of Clir Renewables in 2018 with a wealth of experience in operating software companies. Having founded and subsequently sold two AI companies, Jenny has a background in investment consultancy and electrical engineering.
Having a board with such a wealth of business and technical knowledge and experience ensures Clir Renewables continues on its upward growth trajectory.
It’s not always clear if a turbine is underperforming, but machine learning can assist in finding out.
VANCOUVER, BC: Clir Renewables has released a new product feature which automatically detects underperforming assets and highlights key actions to rectify underperformance.
A wind turbine will never generate its expected output one-hundred percent of the time, and its performance can and almost certainly will change over time. There are various reasons for this. Some are known but cannot be controlled or managed, such as fluctuating inflow conditions. However, occasionally the opposite is the case, . With datasets full of noise from the known reasons, is it possible to extract data to identify the unknown causes? Yes, is the answer.
Using layered machine learning, built on an advanced data model, Clir Renewables has created an underperformance detector for its software solution. This detector works along with other algorithms in the software to analyze the data and . The detector creates a synthetic event when turbine power output is well below the historical mean for that wind speed. This invaluable piece of information helps identify ongoing issues at a turbine, not indicated by the SCADA data, inflow conditions under which the turbine does not perform well, and the duration and lost energy associated with the underperformance. It also highlights a hardware or software configuration change that reduces power performance. The detector removes the noise leaving a clean set of data from which the unknown causes can be deduced, and corrective actions created. Alternatively, if the cause is still unidentified, the owner can approach the manufacturer with the cleaned data looking for answers and solutions.
Selena Farris, Data Scientist at Clir Renewables, said “With noise filled datasets the uncertainty of any conclusions that can be drawn on causes of underperformance will increase significantly, and in a lot of cases, issues can be completely masked by the noise. Utilizing the advances in machine learning, a well-structured data model, and deep domain expertise Clir software provides a tool to reduce this uncertainty, generating actionable insights for owners to increase performance and protect their assets from faults and failures.”
About Selena Farris: Selena has over ten years of renewable energy industry experience with a masters degree specializing in wind energy. Working as a developer, consultant, and utility has given her experience and insight into all stages of the wind farm life cycle. She has managed multiple project portfolios and met campaigns, advising on the most financially responsible development path.