Very-Short-Term Wind Power Forecasting for New-Built Wind Farms Based on Online Sparse Vector Autoregressive Model

Abstract

Very-short-term wind power prediction is of great significance for the safe and stable operation of power system. As a well-known time series model, vector autoregressive (VAR) model is very suitable for very-short-term forecasting, and it can consider the temporal and spatial correlation among multiple wind farms to improve the forecasting accuracy. However, when there are a large number of wind farms, VAR model is prone to over-fitting. Slow calculation speed is also the problem of VAR model. This paper adds regularization terms in estimating coefficients to obtain a sparse coefficient matrix, which helps to avoid over-fitting. Online VAR model is used to improve the calculation speed, and a specific improved online VAR method is proposed for forecasting wind power output from new-built wind farms. This method is tested in a specific case, and the prediction accuracy and calculation speed are better than the traditional VAR model. In addition, the improved online VAR method can accurately predict wind power output of new-built wind farms with little historical data.