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

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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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Do you know how severely derated your portfolio is?

Do you know how severely derated your portfolio is?

Identifying derates on wind turbines can lead to AEP gains.

Asking whether or not a wind farm or individual turbine is curtailed isn’t in a simple yes or no answer. Derates are applied for a variety of reasons, from grid faults or balance of plant to shadow flicker, noise constraints, wildlife management or technical faults. Calculating lost energy due to derating is often difficult, but it is an important metric to measure. The relevant information often isn’t readily available to owners and operators but is hidden in different data streams and formats, such as event logs, park controller data, turbine ten minute SCADA data or set points.

Identifying and extracting the data relating to the derate from all the data received from each turbine is the first step but can take a considerable length of time if done manually. The process is simplified by implementing machine learning and artificial intelligence. Clir Renewables’ software detects and distinguishes between different types of derates for all major turbine manufacturers. With algorithms based on manufacturer specified logic and Clir Renewables’ domain expertise, derates are flagged to the owner. The corresponding losses are also highlighted, enabling owners and operators to calculate the monetary value they are missing.

Using the Clir Explore environment for further investigation can identify patterns across the farm or periods of time for different types of derates. These investigations have identified cases where individual turbines or the whole farm has excessive derates in place. By restructuring the derate plan, AEP gains are achievable. An example is farm-wide grid derating, where OEMs don’t typically use a strategy optimized on production. They often curtail the entire farm as soon as a sample turbine reaches rated output. Using Clir, it’s easy to confirm whether the entire farm is curtailed when curtailing only a few turbines would have been enough. By optimizing the curtailment strategy, the amount of lost energy is reduced, resulting in AEP gains.

Rebecka Klintstrom, Renewables Science Team Leader, said, “Many owners and operators are unaware of the potential gains available if a derate is investigated and its implementation restructured. This is a substantial undertaking if carried out manually; however, using software designed for this purpose makes this task more streamlined.”

One owner with a grid-curtailed farm identified the site was not reaching its expected export level following analysis through the Clir software. Following a discussion with the manufacturer, this curtailment was reduced and the site now reaches its export limit. As a result, the client has realized a 0.2% increase in annual energy production gain at the site.

Clir Renewables celebrates a successful first year in Europe

Clir Renewables celebrates a successful first year in Europe

 From one to fourteen clients in 12 months.

This month Clir Renewables is celebrating the first anniversary of opening its Glasgow office. The Glasgow base was set up, with the appointment of Craig McCall as Director of Europe, to support the expansion of their business in the region. In just a year, Clir Renewables has taken its European client base from a single client with just over 200 MW to exceeding 1GW of assets across fourteen clients.

“The last year was challenging at times as the sole person in the region initially, but it has continuously delivered rewarding moments. A few that stand out are signing the first new European client since the company started, moving to our third office space within a year, and surpassing the 1 GW mark in Europe,” said McCall.

On leaving the stability of a large organization for a start-up, McCall said, “It was extremely clear from the outset that the product was capable of more than I had witnessed in the market.” From a home office, supported by the team in Vancouver, Canada, Clir strategically targeted potential clients across the continent with excellent results securing additional projects in the UK and adding Ireland, Italy, Greece, France, Denmark, and South Africa to the list.

In the final quarter of 2018, Clir moved into its first office space in Glasgow, Scotland when Regional Manager, Brad Hodgson and, Marketing Manager, Mairead McMullin, joined the operation. An additional recruitment drive early in 2019 required a move to a larger office space. The recent addition of Marco Bianchini and Adrian Carnegie to the dedicated European team brings Clir’s global headcount to forty-two with numerous positions set to be filled in the coming months.

Clir Renewables CEO, Gareth Brown, said, “Opening the office in Glasgow was a strategic move to get a larger share of the European market. We have, and continue to build, a highly experienced team in Europe to support our clients in the region. The SaaS market for renewables in Europe is well established, and as a result, it is highly competitive. In addition to providing reporting and greater visibility for owners on their assets, our highly qualified team’s ability to support clients with clearly defined actions to improve performance and reduce costs has set us apart in the global market.”

Clir Renewables now supports over 4 GW of assets globally and continues to expand its European operation through the Glasgow base.

Clir Renewables makes awards shortlist

Clir Renewables makes awards shortlist

Clir Renewables shortlisted for start-up of the year at the European Wind Investment Awards

Wind optimisation software company Clir Renewables is named on the shortlist for the European Wind Investment Awards in the start-up category. The inaugural European Wind Investment Awards celebrate best practice in the European wind industry as it transitions to a zero-subsidy future. Hosted by A Word About Wind, the award winners will be announced at a black-tie dinner in London this October.

Clir Renewables provides a solution developed for wind farm owners, designed by renewable energy experts. The cloud-based AI platform provides asset managers and owners with tools to maximise annual energy production and provide clarity on portfolio performance. Founded in early 2017, the company now supports over 4 GW of assets worldwide with clients typically seeing increases of up to 5% in Annual Energy Production in the first year. Headquartered in Vancouver, Canada, the company opened its European office in Glasgow, Scotland, in 2018.

Craig McCall, Global Director of Business Development, said “We’re delighted to make the shortlist. It is great for the Clir Renewables team to receive industry recognition for all the hard work put in over the last two and a half years. We face tough competition in our category but wish everyone shortlisted the best of luck and look forward to the awards ceremony.”

The start-up category recognises the progression and positive impact of companies who entered the wind industry within the last five years. Clir Renewables is competing with High Speed Energy and Ripple Energy for the prize.


View the full shortlist

Detect the undetected with Clir’s new feature

Detect the undetected with Clir’s new feature

Using peer-to-peer trending, Clir users can now detect changes in data that lead to false analysis.

Clir Renewables has just announced P2P, a new feature for their Data Exploration Environment. Clir’s Data Exploration Environment, Clir Explore, is a unique tool in the renewables space. Using informative data visualizations and pre-populated data variables Clir Explore allows users to view and manipulate streamlined data sets which narrow in on specific performance issues. Clir Explore makes complex analysis simple by providing the specific variables to use when identifying different kinds of anomalous turbine behavior and is comprised of 12 different features each manipulating data differently to identify underperformance. This new feature, P2P, adds to the products suite of capabilities by enabling peer-to-peer trending analysis for turbines. 

Comparing concurrent turbine data is a vital part of a complete wind farm analysis. Clir’s peer-to-peer trending tool makes it easy to detect anomalous behavior and trending over time. For example, it allows our clients to spot anemometer drift and other sensors needing to be recalibrated, as well as subtle differences in power or pitch curve behavior. The P2P tool uses concurrent data, and it’s possible to filter out any non-full performance data. P2P gives analysts a tool to analyze turbine operating data between two selected turbines and was developed to solve the problem of detecting degrading turbine components, to clean up data, and enable better analysis.

The P2P tool allows the user to compare any two turbines in their wind farm and select the variable they want to compare. Among the variables available are power, wind direction, wind speed, blade pitch angle, and generator speed. The results can be colored on time period, nacelle position, or wind directions, which means the user can easily identify if patterns are time depended or direction dependent. The scatter plot and overlaying linear regression, makes it easy to spot outliers. 

Detecting anomalous turbine behavior is easier and faster than before with the P2P tool. “Before our clients started using Clir they didn’t have the data model or tools to get their hands around numerous wind farms. Not only do you need to know what kind of underperformance you are looking for, but you also need to know which variables to use to complete the analysis. With our workbooks, users can use Clir’s catalogue of visualisations and create their own visualizations utilising the Clir data model.” says Gareth Brown, CEO of Clir Renewables.

Want to know more about how P2P could work for your portfolio?