Enhancing Wind and Solar Energy Yield Assessments with Clir Renewables

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Enhancing Wind and Solar Energy Yield Assessments with Clir Renewables

Advanced Loss Modelling and Benchmarking

Clir Renewables offers advanced operational wind and solar energy yield assessments that are backed by a comprehensive global industry dataset. This dataset allows for robust and defensible assumptions, improved p-values, and increased confidence in financial model inputs. The key features of these assessments include improved loss modeling, benchmarked loss assumptions, and modeling the performance of optimizations and upgrades. By utilizing industry-leading energy yield methodology, Clir leverages automated data organization, categorization, and enrichment, along with expert oversight, to reduce uncertainty on energy yield results. Machine learning plays a critical role in calculating potential scenarios, aligning results with best practices and farm-specific considerations to determine accurate future production scenarios.

Accurate Loss Factor Assumptions

One of the major issues in the industry lies in the lack of available data, often resulting in blanket and standard assumptions for technology loss factors and availability. This leads to inaccurate energy yield assessments and underperforming assets. Clir addresses this challenge by utilizing enriched industry data from technology performance, SCADA, monthly reports, and farm-specific data. By incorporating this data into assessments, Clir provides more accurate results, improving confidence in the P50 and P90 values. The use of enriched data also helps persuade independent engineers to upgrade p-values, ensuring that assessments are based on real-world performance.

Peer Group Benchmarking and Future Performance Modelling

Clir leverages global project data to form peer groups based on various factors like region, turbine technology, service provider, and more. This allows clients to compare their wind and solar farm loss factors with industry peers, identifying areas for improvement. By modeling future performance and developing optimization roadmaps, clients can quantify the impact of potential upgrades on energy production. This prioritization of optimizations based on performance impact ensures that clients maximize their energy yield and investment returns.

Enhanced Financial Modelling and M&A Support

Energy yield assessments by Clir play a crucial role in wind and solar investments, directly impacting financial models, project debt, and insurance. By using high-quality datasets and machine learning technology for scenario analysis, Clir reduces uncertainty in energy yield values. This not only benefits financial modeling but also provides valuable insights for M&A transactions and asset development. Leveraging data for robust energy yield assessments ensures that clients receive accurate and actionable information for their investment decisions.

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