Numerical weather prediction
Atmospheric conditions drive the quantity and quality of fuel available to wind and solar power plants. Over the last two decades, numerical weather prediction (NWP) has emerged as the go-to tool for accurate simulation of atmospheric conditions, and is now an indispensable component to energy estimates and predictions. At Veer, we have extensive experience in running NWP models and using their output to drive a range of applications.
Offshore Wind
Offshore wind is exploding globally, but especially in the United States. With capital and operating costs an order of magnitude higher than onshore, combined with limited wind observations at hub-height, the stakes here are high. At Veer, we have worked extensively in offshore wind for both research labs and front-line wind farm developers. We have worked through detailed pre-construction energy estimates and forecasting for our clients and are ready to give our clients the state-of-the-science in offshore wind farm analysis.
Machine Learning
As machine learning continues to explode in applications, all too often it is applied without sufficient domain knowledge – that is, expertise in the industry in which machine learning is being applied. Domain knowledge is vital in choosing the appropriate variables and algorithms needed to build a robust statistical model. This is especially true when it comes to statistical modeling in the field of renewable energy meteorology. At Veer, we have spent years in developing, advocating, and publishing high-impact research in leveraging machine learning in renewable energy. We are ready to take that expertise and put it to work for you.
Asset Performance Optimization
Renewable power plants rely on intermittent and difficult-to-predict energy sources. Understanding the relationship between observed and modeled atmospheric variables is vital in order to benchmark plant performance and identify operational improvements. At Veer, we have spent years working with SCADA and weather data in order to characterize and optimize the relationship with renewable power plants and the atmosphere