Lossless Compression of Wind Plant Data

Substantial quantities of wind plant data are being accumulated as interest and investment in renewable energy grows. These data sets can approach tens of terabytes in size, making their management, storage, manipulation, and transmission burdensome. Lossless compression of the data sets can mitigate these challenges without sacrificing accuracy. This paper develops and analyzes lossless compression algorithms that can be applied to data used in integration studies and data used in wind plant monitoring and operation. The algorithms exploit wind speed-to-wind power relationships, and the temporal and spatial correlations in the data. The Shannon entropy of wind power and speed data is computed to gain insight on the uncertainty of wind power and speed and to benchmark performance of the compression algorithms. The algorithms are applied to the National Renewable Energy Laboratory's Western and Eastern Data Sets and to actual wind turbine data. The resulting compression ratios are up to 50% higher than those obtained by direct application of off-the-shelf lossless compression methods.