Short-Term Wind Speed Forecast Using Measurements From Multiple Turbines in A Wind Farm

Turbine operations in a wind farm benefit from an understanding of the near-ground behavior of wind speeds. This article describes a probabilistic spatial-temporal model for analyzing local wind fields. Our model is constructed based on measurements taken from a large number of turbines in a wind farm, as opposed to aggregating the data into a single time-series. The model incorporates both temporal and spatial characteristics of wind speed data: in addition to using a time epoch mechanism to model temporal nonstationarity, our model identifies an informative neighborhood of turbines that are spatially related, and consequently, constructs an ensemble-like predictor using the data associated with the neighboring turbines. Using actual wind data measured at 200 wind turbines in a wind farm, we found that the two modeling elements benefit short-term wind speed forecasts. We also investigate the use of regime switching to account for the effect of wind direction and the use of geostrophic wind to account for the effects of meteorologic factors other than wind. These at best provide a small performance boost to speed forecast.