Multiple Wind Speed Forecast Using Simple Recurrent Unit

Abstract

In the traditional wind speed forecast of a general wind farm, this farm is often regarded as a whole for point forecasting, and only a single wind speed forecast result is given in a large spatial scale. However, wind turbines are distributed in diverse locations in the wind farm, and wind speeds are often different. Therefore, this paper comes up with a model to forecast the wind speeds of multiple wind turbines by learning the potential relationship between wind speed time series and wind direction time series. First, wind direction feature is preprocessed by trigonometric function. Subsequently Simple Recurrent Unit (SRU) is designed to learn the temporal correlation information. Wind speed and trigonometric wind direction form a three-dimensional feature matrix. Compared with other Recurrent Neural Network (RNN), SRU can greatly reduce the computational cost. Therefore, SRU is used to extract the dynamic information of characteristic data of multiple wind turbines in a wind farm over time. The simulation results with the real data prove that compared with the existing forecast methods using temporal correlation feature, the method not only significantly reduces the computational cost, but also improves the accuracy of forecast.