Characteristics and forecast of short-term wind speed series in the Donghai Bridge wind farm

Abstract

As a sort of worldwide renewable energy, wind energy can reduce environmental pollution and relieve the energy shortage. In order to reduce the adverse effect with the integration of wind energy into electricity grids and the operating cost of power supply system, it is becoming increasingly significant to acquire accurate short-term wind speed forecasts. In this paper, based on the analysis of the measured wind speed data in the Donghai Bridge wind farm, we suggest that the short-term wind speed series has volatility clustering effect and asymmetric effect, and the volatility feed-back effect is not significant. And then the possible causes for this phenomenon are elucidated in detail from the viewpoint of physics. In addition, in order to select the forecast model which is appropriate to the Donghai Bridge wind farm, we use these indexes of error: mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) as the criterion, and compare the prediction accuracy of the five models by quantitative analysis. The results show that ARMA-EGARCH model and ARMA-EGARCH-M model are very close to each other in both single-step and multi-step forecasting, and they are superior to other models. What’s more, with the increase of the number of advance forecasting steps, error growth rate of these two models is low.