使用快速傅里叶变换优化周期参数的EMD-FFT-SARIMA光伏发电预测模型  

EMD-FFT-SARIMA photovoltaic power generation prediction model using fast fourier transform optimization cycle parameters

作　　者：熊川羽 廖晓红 何诗英 陈然 王巍 臧楠 王瀛 肖梦涵 Xiong Chuanyu;Liao Xiaohong;He Shiying;Chen Ran;Wang Wei;Zang Nan;Wang Ying;Xiao Menghan(Economic and Technological Research Institute,State Grid Hubei Electric Power Co.,Ltd.Wuhan 430000,China;Institute of Plasma Physics,Chinese Academy of Sciences,Hefei 230031,China;School of Mechanical and Electrical Engineering,Anhui Jianzhu University,Hefei 230601,China;Institutes of Physical Science and Information Technology,Anhui University,Hefei 230601,China;School of Electrical Engineering and Electronics,Huazhong University of Science and Technology,Wuhan 430074,China;Electric Power Research Institute,State Grid Hubei Electric Power Co.,Ltd.Wuhan 430000,China)

机构地区：[1]国网湖北省电力有限公司经济技术研究院,武汉430000 [2]中国科学院合肥物质科学研究院等离子体物理研究所,合肥230031 [3]安徽建筑大学机械与电气工程学院,合肥230601 [4]安徽大学物质科学与信息技术研究院,合肥230601 [5]华中科技大学,电气与电子工程学院,武汉430074 [6]国网湖北省电力有限公司电力科学研究院,武汉430000

出　　处：《强激光与粒子束》2024年第8期117-123,共7页High Power Laser and Particle Beams

基　　金：北省电力有限公司科技研究项目(521538220003)。

摘　　要：根据分布式能源工业园区的光伏电力单元特点,对园区光伏发电功率预测模型进行优化,为后续的调度策略提供数据支持。针对经验模式分解(EMD)与季节性差分自回归移动平均模型(SARIMA)相组合的EMD-SARIMA预测模型中,原始数据经过EMD分解得到的各固有本征模态函数(IMF)分量周期计算问题,提出加入快速傅里叶变换(FFT)的周期计算方法,建立EMD-FFT-SARIMA光伏发电功率预测模型。再将每个IMF对应的预测结果进行叠加重构得到最终的预测结果。通过预测结果的误差计算可以发现,加入FFT环节后均方根误差(RMSE)从120.6 MW下降到19.3 MW,平均绝对误差(MAE)从52.87 MW下降到12.3 MW。In this paper,the photovoltaic(PV)power prediction model is optimized according to the characteristics of PV output units in distributed energy industrial parks to provide data support for the subsequent dispatching strategy.The EMD-SARIMA forecasting model is a combination of Empirical Mode Decomposition(EMD)and Seasonal Autoregressive Integrated Moving Average(SARIMA).In the model,the problem of determining the period of each IMF component of the signal component is proposed,the period T calculation method incorporating fast Fourier transform(FFT)is proposed,and the obtained period is fed into SARIMA as an input parameter together with the IMF sequence for prediction,which constitutes the EMD-FFT-SARIMA prediction model.Then,the prediction results corresponding to each IMF are superimposed and reconstructed to obtain the final prediction results.The error calculation of the prediction results reveals that the root mean square error(RMSE)decreases from 120.6 MW to 19.3 MW,and the mean absolute error(MAE)decreases from 52.87 MW to 12.3 MW.

关 键 词：经验模式分解 季节性差分自回归移动平均模型 周期计算 固有本征模态函数信号分量 快速傅里叶变换 光伏发电预测 

分 类 号：TM341[电气工程—电机]