Operational energy yield assessments

Clir’s operational wind and solar energy yield assessments are augmented by our global industry dataset, enabling robust and defensible assumptions, improved p-values, and increased confidence in financial model inputs.

Improved modelling icon
Improved loss modelling
Benchmarking loss assumption
Benchmarked loss assumptions
Modelling the impact icon
Modelled performance of optimizations and upgrades

Industry-leading energy yield methodology

Leveraging automated data organization, categorization and enrichment, along with expert oversight, Clir reduces uncertainty on energy yield results by using machine learning to calculate all potential scenarios. Results are aligned with energy yield best practices and farm-specific considerations to determine which scenario accurately represents the farm’s future production.

Diagram of the energy yield process by Clir
Images of a sample report with a graph of the turbine availability loss factors on top

Improved loss factor assumptions

Due to a lack of available data, the industry often uses blanket, standard assumptions for technology loss factors and availability. This leads to inaccurate energy yield assessments and assets underperforming compared to predictions. Clir uses enriched industry data from technology performance, SCADA and monthly reports, alongside farm-specific data, for more accurate assessments. This improves confidence in the P50 and P90, offering evidence to persuade independent engineers to upgrade p-values.

Benchmarked loss factors

Global project data allows Clir to form peer groups — based on region, turbine technology, service provider, asset manager and more — to compare and understand wind and solar farm loss factors. Clients can see how their farm is performing compared to peers, and understand and rectify the losses that are impacting farm performance. This enables more robust energy yield assessments and the identification of high loss categories with potential for improvement.

Image of a wind turbine in the snow with a graph of the benchmarked loss factors
Graph of the scenario identifier

Modelling future performance

Clir develops optimization roadmaps for clients using farm data, industry insights and best practices. This includes data on the performance of available upgrades. By modelling the impact of optimizations or upgrades on future energy production, clients can quantify the impact of these changes on energy yield metrics. Implementation of optimizations and upgrades can be prioritized by what generates the biggest performance impact.

Leverage Clir’s energy yield assessments for


Energy yields output summary table

Learn how to leverage data for robust energy yield assessments.