基于WRF模式的风电场短期风速集成预报方法研究

风能始源于大气的运动,具有很大的随机性和间歇性。风速预测是风电场风功率预测的基础,其准确性具有重要的意义。对于复杂地形条件下,风速的预报一直是各国研究的难点和重点。为了提高风电场短期风速预报的准确性,本研究采用多种边界层参数化方案来集成预报风速,将各单一边界层参数化方案预报的风速及相应的实测风速数据,应用随机森林算法建立集成预报模型,对风电场的短期风速进行集成预报研究。试验结果表明,采用集成预报风速方法,预报的风速误差相比于单一边界层参数化方案预报的风速误差明显减小,对研究区域的风速、风向等气象要素有着较好的模拟效果,能够有效提高风速预报的准确率。

Integrated Forecasting Method Research of Short Term Wind Speed in Wind Power Plant Based on WRF Model

Wind energy is originated from the movement of the atmosphere with great randomness and intermittency. Wind speed forecasting is the basis of wind power forecasting in wind power plant, and its accuracy is of great significance. For complex terrain conditions, wind speed forecasting has been the difficulty and focus of research in all countries. In order to improve the accuracy of short term wind speed forecasting of wind power plant, this paper uses different boundary layer parameterization schemes to forecast wind speed. The wind speed of each single boundary layer parameterization scheme prediction and the corresponding measured wind data are used to establish the forecast model by random forest algorithm to study the short term wind speed in wind power plant. The experimental results show that with the integrated forecasting method of wind speed, the wind speed prediction error is significantly smaller compared to the single boundary layer parameterization scheme. Also, it has a good simulation effect for wind speed and wind direction and other meteorological elements in the study area and can effectively improve the accuracy of wind speed forecasting.