- WindESCo, the pioneer in renewable energy optimization, today announced that it has received approval from DNV for its Energy Improvement Analysis Method 3.0.
- The method uses machine learning to evaluate energy improvement of wind turbines following upgrades to underperforming turbines.
- Using machine learning, WindESCo is able to compare how the performance of adjusted turbines compares to control turbines.
A WindESCo news report says that
The DNV review
The DNV review included a detailed look at the methodology, data selection, uncertainty quantification, and reporting requirements. Nathan Post, Sr. Director of R&D at WindESCo noted, “This work marks a significant advancement in the state-of-the-art for measuring performance improvements on wind plants.”
WindESCo’s energy improvement analysis
This latest iteration of WindESCo’s energy improvement analysis replaces a version approved by DNV in 2019.
The updates were made to better-measure small changes in wind turbine performance and improve accuracy.
The model is currently being utilized at over 20 wind farms around the world in an effort to measure improvement to annual energy production (AEP) made through WindESCo’s proprietary algorithms and software solutions.
The approval by DNV of its most current energy improvement analysis is an important proof point as the company seeks to scale its services.
What sets WindESCo apart in the industry is that its analytics do more than identify problems at wind turbines and farms, by also providing solutions to fix them and subsequently measure the results,
The vetting by DNV validates its “measure” procedures are scientifically sound.
WindESCo possesses deep knowledge of wind science, engineering, and machine learning and has worked together to translate that knowledge to a scalable solution to serve the customers.
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