基于动态谐波回归的超短期风电功率预测

准确的风电功率预测对电力系统的安全稳定运行十分重要。从风功率统计特征出发，提出进行风电功率超短期预测的动态谐波回归方法。首先利用风电功率与不同高度风速的三次函数关系构建回归模型；然后采用自回归移动平均 模 型（auto regressive integrated moving average model，ARIMA）对回归的残差建模来充分利用风电功率时间序列的历史信息；最后针对风电功率的日季节性特点，引入傅里叶级数形成最终预测模型。经风电场实际数据计算验证表明，该方法有效弥补了ARIMA方法和回归方法的不足，减小了风电预测均方根误差（root mean squared error，RMSE），提高了风电预测精度。通过和持续法、ARIMA 2种现有预测方法比较，验证了所提模型具有更高的预测精度，说明该方法具有一定的实际应用价值。

Very-short-term wind power forecasting based on dynamic harmonic regression

Accurate wind power forecasting is very crucial for the security and stability of power system operation. From a statistical standpoint, a dynamic harmonic regression method is proposed for very-short-term wind power forecasting. Cubic polynomials between wind power and wind speed at different heights are used to construct the regression model. Then, autoregressive integrated moving average model is proposed to model the regression residual in order to fully use historical information of wind power time series. Finally, according to the daily seasonal characteristics of wind power, fourier series is introduced and the final model is established. Results from real-world wind farms show that this method can effectively improve the traditional autoregressive integrated moving average model and regression methods, which can reduceroot mean squared error and improve the prediction accuracy. Compared with two commonly-used existing approaches, persistence and autoregressive integrated moving average model, the proposed model is verified to have higher prediction accuracy, indicating that this method has certain practical application value.