Evaluating the potential of short-term instrument deployment to improve distributed wind resource assessment

Abstract

Abstract. Distributed wind projects, which are connected at the distribution level of an electricity system or in off-grid applications to serve specific or local energy needs, often rely solely on wind resource models to establish wind speed and energy generation expectations. Historically, anemometer loan programs have provided an affordable avenue for more accurate onsite wind resource assessment, and the lowering cost of lidar systems have shown similar advantages for more recent assessments. While a full twelve months of onsite wind measurement is the standard for correcting model-based long-term wind speed estimates for utility-scale wind farms, the time and capital investment involved in gathering onsite measurements must be reconciled with the energy needs and funding opportunities that drive expedient deployment of distributed wind projects. Much literature exists to quantify the performance of correcting long-term wind speed estimates with one or more years of observational data, but few studies explore the impacts of correcting with months-long observational periods. This study aims to answer the question of how low can you go in terms of the observational time period needed to make impactful improvements to model-based long-term wind speed estimates. Three algorithms, multivariable linear regression, adaptive regression splines, and regression trees, are evaluated for their skill at correcting long-term wind resource estimates from the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) using months-long periods of observational data from 66 locations across the United States. On average, correction with even one month of observations provides significant improvement over the baseline ERA5 wind speed estimates and produces median bias magnitudes within 0.21 m s-1 of the median bias magnitudes achieved using the standard twelve months of data for correction. However, in cases when the shortest observational periods (one to two months) used for correction are misrepresentative of the longer-term trends in the wind resource, the resultant long-term wind speed errors are worse than those produced using ERA5 without correction. Summer months, which are characterized by weaker relative wind speeds and standard deviations for most of the evaluation sites, tend to produce the worst results for long-term correction using months-long observations. The three tested algorithms perform similarly for long-term wind speed bias; however, regression trees perform notably worse than multivariable linear regression and adaptive regression splines in terms of mean absolute error and correlation when using six months or less of observational data for correction.