基于灰色Verhulst模型的短期风速预测研究

Research on Grey Verhulst Models for Short-term Wind Speed Prediction

风速预测在保持风力发电系统稳定、风力发电功率预报、风电并网接入等方面都具有重要的应用。为了提高风速预测的精确性,提出了一种基于新陈代谢思想的灰色Verhulst模型的风速预测方法。该方法首先对灰色GM(1,1)模型和灰色Verhulst模型进行改进,其次引入了"新陈代谢"的概念,即在每一次风速预测的迭代过程中用风速真实序列的最新数据替代原有序列的最老数据,在不增加迭代维数的条件下,不断更新灰色Verhulst模型,将更新后的Verhulst模型进行优化,实现精确的风速预测。通过对实际风场风速数据的采集,运用该灰色Verhulst模型预测风速。实践仿真结果表明,与传统预测方法相比,此方法能有效的降低短期风速预测的误差,应用前景十分广阔。

Wind speed forecast technology is playing an increasingly important role in maintaining the wind power system＇s stability, forecasting wind power generation and connecting electric grid. In the paper, the forecast problems of wind speed are considered. In order to enhance the rediction accuracy of the wind speed, a grey Verhulst model based prediction method is introduced to forecast wind speed in a short period. Moreover, a metabolic Verhulst model is presented based on the iterative thought to update the prediction mod el at each calculation, through replacing the oldest data by the current new data, without increasing the dimension of iterative wind speed vector. Collect the real data from wind farm and use the proposed method to forecast wind speed. The simulation results show that the proposed metabolic Verhulst method can effectively predict the wind speed in short term.