基于WRF数值模式的SDAE-SVR风速预测模型研究
PDF下载 (339)陈巧特,符冉迪,何彩芬.基于WRF数值模式的SDAE-SVR风速预测模型研究[J].宁波大学学报(理工版),2020,33(2):47-53.DOI:
CHEN Qiaote,FU Randi,HE Caifen.Wind speed forecasting model of SDAE-SVR based on WRF numerical pattern[J].Journal of Ningbo University(Natural Science & Engineering Edition),2020,33(2):47-53.DOI:
| Title: | Wind speed forecasting model of SDAE-SVR based on WRF numerical pattern |
| 作者: | 陈巧特, 符冉迪, 何彩芬 |
| Author(s): | CHEN Qiaote, FU Randi, HE Caifen |
| 关键词: | 天气研究和预报模式; 支持向量回归; 堆栈降噪自动编码; 深度学习; 风速预测 |
| Keywords: | weather research and forecasting pattern; support vector regression; stacked denoising auto-encoder; deep learning; wind speed forecasting |
| 分类号: | TP391 |
| 文献标识码: | A |
| 摘要: | 风速预测是风力预报中的核心与基础, 采用天气研究和预报(Weather Research and Forecasting, WRF)模式进行风力预报往往存在风速预测误差较大的问题. 为了提高风速预测精度, 提出了一种基于深度学习和支持向量回归(Support Vector Regression, SVR)相结合的风速预测模型. 该模型以WRF模式预报输出的多种气象变量为基础, 结合气象自动观测站传感器的实测风速, 引入堆栈降噪自动编码(Stacked De-noising Auto-Encoder, SDAE)深度网络来学习样本数据中隐含的深度特征, 然后将该深度网络最后一层输出的深度特征置入回归器SVR中, 利用SVR良好的回归预测性能对WRF模式预报的未来1h风速进行预测订正. 结果表明: 所建立的SDAE-SVR风速预测模型具有较高的风速预测精度, 在对典型日的WRF模式预报未来1h风速的预测订正中, 其平均百分比误差与均方根误差仅为8.28％与0.8 066 m<span style="font-family: "Times New Roman"; font-size: 10pt;">·s-1. |
| Abstract: | Wind speed forecasting is essential for wind forecasting. Using the Weather Research and Forecast (WRF) pattern in wind speed forecasting constantly comes up with some problematic issues with it. To tackle the problem in order to improve the accuracy of wind speed prediction, a forecasting model combining deep learning with Support Vector Regression (SVR) is proposed in this paper. The input data for the model is collected based on the various meteorological variables generated from the WRF pattern, and are combined with the real wind speed collected from the sensor of automatic meteorological observation. Stacked De-noising Auto-Encoder (SDAE) deep network is constructed to learn the intrinsic data characteristic, then the last layer of the deep network output characteristics is fed into SVR, and the good regression forecasting performance of the SVR is used to correct the future 1h wind speed forecasting of the WRF pattern. The experimental results show that the established SDAE-SVR wind speed forecasting model performs with higher accuracy in correcting the future 1h wind speed prediction of WRF pattern for typical days, and the mean average percentage error as well as root-mean-square error are limited within the range of 8.28％ and 0.8 066 m<span style="font-family: "Times New Roman"; font-size: 10pt;">·s-1. |
| 参考文献 /References: | [1] Kim Y J, Jeong M A, Son N R. Forecasting of short-term wind power generation based on SVR using characteristics of wind direction and wind speed[J]. The Journal of Korean Institute of Communications and Information Sciences, 2017, 42(5):1085-1092. [2] 叶小岭, 顾荣, 邓华, 等. 基于WRF模式和PSO- LSSVM的风电场短期风速订正[J]. 电力系统保护与控制, 2017, 45(22):48-54. [3] 邓华, 张颖超, 顾荣, 等. 基于PCA-RBF的风电场短期风速订正方法研究[J]. 气象科技, 2018, 46(1):10-15. [4] 钱斌凯, 何彩芬, 金炜, 等. 基于支持向量机的多因子风速预测[J]. 宁波大学学报(理工版), 2018, 31(3):14- 19. [5] 张颖超, 肖寅, 邓华. 基于ELM的风电场短期风速订正技术研究[J]. 气象, 2016, 42(4):466-471. [6] 崔江, 唐军祥, 龚春英, 等. 一种基于改进堆栈自动编码器的航空发电机旋转整流器故障特征提取方法[J]. 中国电机工程学报, 2017, 37(19):5696-5706; 5847. [7] Cho K, Courville A, Bengio Y. Describing multimedia content using attention-based encoder-decoder networks [J]. IEEE Transactions on Multimedia, 2015, 17(11): 1875-1886. [8] 侯文擎, 叶鸣, 李巍华. 基于改进堆叠降噪自编码的滚动轴承故障分类[J]. 机械工程学报, 2018, 54(7):87-96. [9] Vincent P, Larochelle H, Lajoie I, et al. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion[J]. Journal of Machine Learning Research, 2010, 11(12):3371-3408. [10] Mi X W, Liu H, Li Y F. Wind speed forecasting method using wavelet, extreme learning machine and outlier correction algorithm[J]. Energy Conversion and Management, 2017, 151:709-722. [11] Wang J J, Zhang W, Wang J Z, et al. A novel hybrid approach for wind speed prediction[J]. Information Sciences, 2014, 273(18):304-318. [12] Santamaria-Bonfil G, Reyes-Ballesteros A, Gershenson C. Wind speed forecasting for wind farms: A method based on support vector regression[J]. Renewable Energy, 2015, 85:790-809. |
| 备注/Memo: | 收稿日期: 2019－03－04. 宁波大学学报（理工版）网址: http://journallg.nbu.edu.cn/ 基金项目: 国家自然科学基金（61471212）; 浙江省自然科学基金（LY16F010001）; 宁波市自然科学基金（2016A610091, 2017A610297）. 第一作者: 陈巧特（1994－）, 男, 湖北黄冈人, 在读硕士研究生, 主要研究方向: 风速预测. E-mail: 864309372@qq.com 宁波大学学报（理工版）网址：http://journallg.nbu.edu.cn/*通信作者: 符冉迪（1971－）, 男, 浙江宁波人, 副教授, 主要研究方向: 数字图像处理. E-mail: furandi_nbu@163.com |
