Informed project development

A lack of transparency on technology performance under real world conditions hampers pre-construction decision making. Drawing on intelligence from comparable technologies in a global dataset provides empirical evidence to support decision making and contracting in the asset development phase.

Realistic expectations of performance

Clir provides a comprehensive understanding of turbine performance risks by using energy yield loss factors and performance KPIs. Owners gain insight into controllable lost energy, contractual availability and the adoption curve of technologies on the market, enabling a deeper understanding of project performance compared to peers. This can better inform turbine and technology selection, while enabling improved optimization and O&M strategies.

Image of wind turbines and comparisons of three OEMs
Graph comparing three different OEMs throughout 6 years of data availability loss

Refine energy yield loss assumptions

Clir presents impactful energy yield loss factors for each turbine model, benchmarked against regional data. This data-driven approach allows clients to compare the expected performance of each turbine model through the lens of their predicted energy production, and independent engineers to refine and improve pre-construction energy yield assessments.

Evaluate environmental risks

Integrating industry data with external sources, such as weather data, improves understanding of the environmental risks at a site and the turbine’s ability to withstand them. With insight into how susceptible the turbines are to damage from natural catastrophe and environmental conditions, owners can understand the inherent site risks and select the most suitable turbines.

A topographical image of a mountainous area with dots marking the site and a graph showing loss factors
A graph showing high and low risk with a second graph showing energy base availability

Improve contractual and operational practices

With insights into turbine and site risks, owners can develop contractual and operational practices to protect projects against risk exposure. Clir’s data enables recommendations on turbine service agreements and operational and maintenance contracts. This can improve warranty terms and inspections, scheduled maintenance, availability agreements, component replacement caps and extreme weather protocols.

Understand OPEX expectations

Data on the severity of site risks, technology selection and approach to contractual practices offers insight into the overall operational practices. This can lend a high-level perspective on how insurers and lenders may approach the project through a view on the insurance and financial implications for the site, technology, and contractual decisions. Each turbine’s risk is ranked and the base-level rate premium expected for the site is highlighted.

Bar graph of high and low risk of different risk categories
Diagram showing expected gross production and a volume modelling

Optimize offtake agreements

Clir uses intelligence from over 200 GW of operational assets to account for the expected energy losses of the farm. The validated gross model is trained on peer loss data — based on technology type, asset age, location, size, environmental conditions and other factors — to simulate the net energy production uncertainty bands in any hour, day, month, quarter or year.

Interested in insights for assets under development?