集合选优方法在短期风功率预测中的应用研究

Application of Ensemble Selection Method in Short-term Wind Power Forecasting

关键词:  短期风速预测  短期功率预测  集合预报  机器学习  支持向量回归

Key words:  short-term wind speed prediction  short-term wind power prediction  ensemble forecast  machine learning  support vector regression

摘要:

为提高短期风速及功率预测的准确率，减小风电不确定性对电网系统的影响，尝试利用预测窗口期的风速观测进行数值天气预报的集合成员选优，挑选和实际风速更接近的相似预报成员，并构成选优集合进行机器学习模型的训练和测试。相较仅使用集合平均的常规方法，该方法考虑了不同集合成员之间的预报差异，避免了引入误差较大的集合成员，从而有利于改善预报风速偏差。利用不同海拔高度、不同地形特征的河南、甘肃两个风电场中不同集合的表现及敏感性试验结果，确定风电场最佳选优集合数量。相较于集合平均的结果，集合选优方案在不同天气过程中能较好地预报风速的起降，与实际风速更接近，且海平面气压场整体更接近ERA5。对不同风电场进行连续十一个月的风速及功率预测对比试验，结果表明，集合选优方法预报的风速日变化形态和月均风速较原集合平均方法均有改善。分析两个风场不同时长范围、不同速率变化的上坡风和下坡风观测数据可知，在0~2h及2~4h内，风速变化为2~4m/s的个例最多。对比集合平均结果，集合选优方案对于该类型上、下坡风的预测精度均有较为明显的提升。利用机器学习算法对选优集合预报进行训练，能进一步降低风速的绝对偏差和均方根误差，从而有效改善功率预测精度。

Abstract:

To improve the accuracy of short-term wind speed and power forecasts as well as reduce the impact of wind power uncertainty on the grid system, this study attempted to use wind speed observations to select the optimal numerical forecasting ensemble members that closely matched the actual wind speed within the forecast window. Those selected ensemble members then formed an optimized ensemble for training and testing machine learning models. Unlike the conventional method that relied solely on ensemble averaging, this method considered the forecast discrepancies among different ensemble members, avoided the introduction of members with large errors, and thus helped to improve wind speed prediction. The optimal number of ensemble members for wind farms with different altitudes and terrains in Henan and Gansu was determined based on the results of ensemble performance and sensitivity experiments. Comparative analyses demonstrated that the ensemble selection forecasts outperformed ensemble averaging in predicting wind speed fluctuations during different weather processes, closely aligning with actual wind speed observations. The sea-level pressure field estimates generated by the ensemble selection method exhibited a higher level of agreement with ERA5 data. Evaluation of wind speed and power prediction over eleven consecutive months in different wind farms showed that the ensemble selection method improved the accuracy of diurnal variation and monthly average wind speed compared to the original ensemble averaging method. Analysis of the observed data of upslope and downslope winds with different durations and speed changes in two wind farms showed that there were the most wind speed changes of 2 —4 m s-1 within 0—2 h and 2—4 h. Compared to ensemble averaging, the ensemble selection method significantly improved the prediction accuracy for these upslope and downslope winds. Furthermore, using the machine learning algorithm to train the optimal selection ensemble can further reduce the absolute deviation and root mean square error of wind speed, thereby effectively improving the accuracy of power prediction.