Three recent wind resource datasets are assessed for their skills in representing annual average wind speeds and seasonal, diurnal, and interannual trends in the wind resource in coastal locations to support customers interested in small and midsize wind energy.

A total of 12 months of onsite wind measurement is standard for correcting model-based long-term wind speed estimates for utility-scale wind farms; however, the time and capital investment involved in gathering onsite measurements must be reconciled with the energy needs and funding opportunities for distributed wind projects. This study aims to answer the question of how short you can go in terms of the observational time period needed to make impactful improvements to long-term wind speed estimates.

This study examines the potential for distributed wind energy across the contiguous United States by leveraging a novel modeling approach that utilizes a high-resolution dataset and analyzes over 150 million parcels. Modeling results reveal substantial opportunities for energy generation using distributed wind technologies. Additionally, findings reveal a substantial increase from prior modeling results in estimated technical and economic potential for distributed wind.

Adoption of distributed wind turbines for energy generation is hindered by challenges associated with siting and accurate estimation of the wind resource. This study evaluates classic and commonly used methods alongside new state-of-the-art models derived from simulations and machine learning approaches using a large dataset from the Netherlands. We find that data-driven methods are most effective at predicting production at real sites and new models reliably outperform classic methods.

The small wind community relies on simplified wind models and energy production simulation tools to obtain energy generation expectations. We gathered actual wind speed and turbine production data across the US to test the accuracy of models and tools for small wind turbines. This study provides small wind installers and owners with the error metrics and sources of error associated with using models and tools to make performance estimates, empowering them to adjust expectations accordingly.