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Abstract:
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Large-scale integration of wind energy into electric utility systems requires accurate short-term wind speed forecasts. At these horizons, statistical models that account for spatial and temporal information have demonstrated improved accuracy over both physical models and statistical models that ignore spatial information. Off-site information can be incorporated by modeling wind speeds conditional on a set of regimes that capture the predominant wind patterns within a geographic region. Identifying these regimes is a crucial model-building step. We propose a new forecasting method that relies on regimes identified by fitting a Gaussian mixture model (GMM) to the wind vector, and we build regimes based on a single site, a local average of sites, and a region-wide average. Models with GMM-identified regimes are compared to three state-of-the-art reference models that each account for wind regimes differently. The models are evaluated at 30-min, 1-hr, and 2-hr ahead horizons at ten sites across the Pacific Northwest. GMM regimes based on local information produce the best forecasts and have a significantly improved accuracy at a region-wide level over the reference models.
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