| 多种风电场风速预报订正方法的适用性研究与集成应用 |
| |
| 引用本文： | 徐丽娜,申彦波,冯震,叶虎.多种风电场风速预报订正方法的适用性研究与集成应用[J].干旱区地理,2022,45(4):1114-1124. |
| |
| 作者姓名： | 徐丽娜 申彦波 冯震 叶虎 |
| |
| 作者单位： | 1.内蒙古自治区气象服务中心,内蒙古 呼和浩特 0100512.中国气象局公共气象服务中心,北京 1000813.中国气象局风能太阳能资源中心,北京 100081 |
| |
| 基金项目： | 内蒙古自治区自然科学基金项目(2021MS04002);中国气象局公共气象服务中心创新基金项目(M2021009) |
| |
| 摘 要： | 以内蒙古中部某风电场为实验风电场,采用随机森林（Random forest,RF）方法、相似误差订正（Analogue correction of errors,ACE）方法以及概率密度匹配方法（Probability density function matching method,PDF）分别对风电场风速预报进行订正及适用性研究。结果表明：3种方法在各季均对中尺度天气预报模式（Weather research and forecasting model, WRF）风速预报具有不同程度的订正效果,RF方法可以有效改善WRF误差较大的问题,但兼具误差过分放大情况,ACE方法和PDF虽然对较大误差的改善能力不及RF方法,但是能够较好地控制误差过分放大问题。此外,3种方法针对小于5 m·s-1的小风速段,订正效果不理想,随着风速的增加,订正能力逐渐增强。参照预报模型各自的优势,尝试开展多种预报模型的分风速等级集成应用,可以对不同风速等级下的WRF预报起到较好的改善作用。
|
| 关 键 词： | 风速预报 随机森林 相似误差订正 概率密度匹配方法 集成预报 |
| 收稿时间： | 2021-11-18 |
Applicability research and integrated application of various correction methods for wind speed forecast in a wind farm |
| |
| XU Li&#x,na,SHEN Yanbo,FENG Zhen,YE Hu.Applicability research and integrated application of various correction methods for wind speed forecast in a wind farm[J].Arid Land Geography,2022,45(4):1114-1124. |
| |
| Authors: | XU Li&#x na SHEN Yanbo FENG Zhen YE Hu |
| |
| Affiliation: | 1. Inner Mongolia Service Center of Meteorology, Hohhot 010051, Inner Mongolia, China2. Public Meteorological Service Center of China Meteorological Administration, Beijing 100081, China3. Wind and Solar Energy Resources Center of China Meteorological Administration, Beijing 100081, China |
| |
| Abstract: | The method used for evaluating the correction effect of a model is very important for improving the short-term wind speed forecast. At present, the comparison of the correlation coefficient and error between the corrected and forecasted wind speeds in the research period is generally adopted. This evaluation method often ignores the limited correction ability of most statistical correction methods in low and high wind speed sections. Taking a wind farm in central Inner Mongolia, China as the experimental site, this study uses the three methods of random forest (RF), analog correction of errors (ACE), and probability density function matching method (PDF) to correct the wind speed forecast in the experimental wind farm. A correlation coefficient and error analysis of the whole research period is performed. Wind speed is classified into cut-in wind speed, low, high, and full generating wind speeds, and cut-out wind speed. The applicability study of the three models in each grade is then conducted. The results show that the three methods have different degrees of correction effects on the weather research forecast (WRF) of wind speed in each season. The average absolute error promotion rates in sequence are 6.7%-36.7%, 11.1%-34.7%, and 3.7%-20.6% after correction via the RF, ACE, and PDF methods, respectively, in the model validation period. The RF method can effectively improve the large error problem of the WRF, but it also has the error overamplification problem. Although the ACE and PDF methods have less ability for improving the large error compared with the RF method, they can better control the error overamplification problem. For the small wind speed section of less than 5 m·s-1, the correction effect of the three methods is not ideal. The original WRF forecast and PDF method are better than the RF and ACE methods. With the wind speed increase, the WRF forecast error significantly increases, and the correction ability of the three methods gradually increases. The RF and ACE methods are better than the PDF methods in the wind speed level of 5,16). The correction ability of the ACE method begins to decline, whereas that of the PDF method begins to improve in the wind speed level of 16,+∞). According to the respective advantages of the forecast models, the integrated application of various forecast models considering the wind speed forecast level is attempted to improve the WRF forecast under different wind speed levels. |
| |
| Keywords: | wind speed forecast random forest analogue correction of errors probability density function matching method integrating forecast |
|
| 点击此处可从《干旱区地理》浏览原始摘要信息 |
|
点击此处可从《干旱区地理》下载全文 |
|
