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Renewable energy data: a question of quality, not just quantity

Image: Adobe Stock/ metamorworks

Now is the time to invest in data infrastructure to ensure a consistent standard of high-quality renewable energy data, says Gareth Brown, CEO & Co-Founder of Clir Renewables.

With the digitalisation of renewable energy continuing to accelerate, the collection of large volumes of data to inform decision-making is a key point of focus for many wind and solar developers, operators and investors. And this makes sense: it takes millions of data points — including SCADA data, project metadata, and operations and sensor data — to provide a holistic view of asset performance, health and risk.

However, effectively managing and extracting actionable insights from these broad and often disparate pools of information continues to be a complex task that few are well-placed to solve without the right tools. ‘Quantity’ is fundamental; but behind nacelles and boardroom doors, ‘quality’ is where the real breakthroughs are happening in the world of renewable energy data.

For one, data quality is critical for truly understanding the performance of renewables assets. Accuracy can vary due to equipment issues, weather conditions and human error, leading to guesswork to fill in gaps and extrapolate on asset performance. Also, the different formats, units and protocols used by various asset managers and their technologies can make standardisation difficult, impeding understanding, and the creation of streamlined operational and financial strategies.

These challenges have profound implications for asset development, operations, and mergers and acquisitions. Quality data is essential for effective site and technology selection, as it provides insights into a site’s energy yield potential. Automated modelling and labelling facilitate accurate data comparisons between assets by smoothing out differences caused by external reporting, leading to the ability to improve projects based on historical and industry optimisation strategies. Precise project valuations and risk assessments, supported by benchmarking, are crucial for investors and developers engaged in M&A activities.

We will dive into each of these three strands of the renewable energy project lifecycle and explore how digital solutions are helping owners stay on course with the support of high-calibre data strategies.

Asset development

In an increasingly competitive market, developers are faced with a dynamic risk profile, considerable practical challenges and uncertainty in the successful execution of renewables projects. Before a wind farm can be built, its project trajectory is based on pre-construction assumptions about asset lifetime and value. These will inform site and technology selection, as well as contracting decisions. With farms and turbines being built larger than ever before, new operational challenges are emerging that operators are unfamiliar with.

Assumptions must be made about energy yield loss factors — the difference in the amount of electricity that an asset will realistically produce over a given period versus its maximum capacity, due to technological or environmental limitations. Net P50 is the forecast of the average annual energy production of a wind farm over the first 50% of its life, and it is critical for achieving favourable financing and insurance terms.

Knowing how to manage the inherent uncertainty of these assumptions is contingent on having accurate data. Developers need insights on technical manufacturer issues, operational maturity timelines and risk factors based on specific site conditions. Regional and manufacturer variations will result in differences in loss factors and production, and future operations and maintenance (O&M) strategies are devised based on these, so data drawn from them must be precise.

By accessing new data streams, such as environmental and extreme weather data, grid and technology availability, degradation metrics, trends in equipment failure rates and repair intervals, and sub-optimal performance, developers can gain invaluable access to accurate and extensive market intelligence to ensure greater certainty in their asset development decision-making. Unfortunately, data sharing and access is still limited, meaning that many developers need to leverage third-party providers to obtain data for these insights.

By leveraging data for accurate assumptions, they are able to create a more transparent and reliable project trajectory. This can support them in the process of securing the necessary initial funds for project construction, while ensuring positive returns on investment in the operational phase.


Machine learning algorithms are vital for processing complex renewable energy data. These algorithms can identify patterns to understand underperformance and loss factors to help owners optimise assets. They provide accurate insights and enhance data interpretation; ensuring proper data labelling is a key part of streamlining this process.

However, problems can arise when developers rely on an array of different asset managers with different platforms to collect this data. Sprawls of market, financial, performance and asset health data are drawn from these various sources and fed into backroom ‘data lakes’: central repositories which are often full of data with different labels, levels of granularity and even file types.

This results in a lack of standardisation across a developer’s portfolio. It means that in the boardroom, the only visibility investors have of their assets is from third-party reports, in varied formats and with no common ‘language’ of comparison between assets or with industry benchmarks.

To improve the oversight of asset performance, these stakeholders need machine learning and algorithms that can collect and standardise operational data, clean and label it while providing a range of services including benchmarking, KPI display and reporting — enabling transparency into operational performance.

This allows them to verify independent external reports and create accountability by providing evidence in discussions and disputes with service providers, OEMs and other third-party vendors. The establishment of industry-wide uniformity may aid with this in the future, but for now, software platforms which can perform these tasks must be relied on.

Technologies that leverage industry data to help owners rapidly find areas of optimisation using insights from peer wind and solar farms are also key to improving operational practices. By using technology for optimisation strategies based on best practices, owners can not only identify which optimisation opportunities exist for the farm, but the potential gains, and next steps to implement and verify these gains.

Mergers & acquisitions

M&A activity relies on accurate valuations. How much are your assets worth? How many operating years are left? Arriving at the negotiating table with these figures is a must, but being armed with context — that is, knowing how much your assets are worth when compared to the industry at large — is also key.

Benchmarking compares the performance of renewable energy assets against hundreds of gigawatts of historical industry data. By setting performance targets and identifying areas for improvement, benchmarking not only aids in asset development and site selection, it gives buyers and sellers a consistent picture of what ‘good’ looks like throughout the pre-binding offer, binding offer and post-acquisition phases.

In the pre-binding offer stage, buyers can use market intelligence to inform their due diligence activities as they build towards a more informed bid, while sellers are able to provide them with full transparency about asset performance.

The binding offer will then come about through a synthesis of that intelligence with the available site-specific data. For buyers and sellers, this has the advantage of increasing the certainty of asset potential, making valuations from both sides more accurate and bolstering the confidence of both parties in their technical assumptions.

Post-acquisition, quality data from benchmarking helps to draw up optimisation roadmaps for continued operational success: optimising production and operational expenditure for minimum cost and risk, as well as receiving assessments and recommendations for upgrades. This ensures that buyers are getting the best performance from their new assets.


For the big players of most developed wind and solar markets, data is not in short supply. Ensuring its usability and combining it in ways that make sense for various types of stakeholders in asset development, operations and M&A are the key challenges. The top decision-makers of our industry need transparency into asset performance to make data-driven operational decisions. That’s why there has never been a better time to invest in data infrastructure and benchmarking to ensure a consistent standard of high-quality data.

Picture of Gareth Brown
Gareth Brown
CEO & Co-Founder of Clir Renewables

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