考虑数据分解和进化捕食策略的BiLSTM短期光伏发电功率预测  

BiLSTM SHORT-TERM PHOTOVOLTAIC POWER PREDICTION CONSIDERING DATA DECOMPOSITION AND EVOLUTIONARY PREDATION STRATEGIES

作　　者：焦丕华 蔡旭[2] 王乐乐 陈佳佳[1] 曹云峰[2] Jiao Pihua;Cai Xu;Wang Lele;Chen Jiajia;Cao Yunfeng(School of Electrical and Electronic Engineering,Shandong University of Technology,Zibo 255000,China;School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)

机构地区：[1]山东理工大学电气与电子工程学院,淄博255000 [2]上海交通大学电子信息与电气工程学院,上海200240

出　　处：《太阳能学报》2024年第2期435-442,共8页Acta Energiae Solaris Sinica

基　　金：国家自然科学基金(51837007);山东省重点研发计划(2019JZZY020804)。

摘　　要：提出一种考虑数据分解和进化捕食策略的双向长短期记忆网络(BiLSTM)短期光伏发电功率预测模型。首先,针对大量高频分量且频率成分复杂的原始光伏发电功率,通过数据分解理论,提出互补集合经验模态分解(CEEMD)与矩阵运算的奇异值分解(SVD)融合的(SVD-CEEMD-SVD,SCS)方法,实现光伏发电功率数据的二次降噪。然后,建立进化捕食策略(EPPS)和BiLSTM的组合预测模型,以更好地挖掘模型的内在特征,提升功率预测精度。最后,以山东某地区实际光伏电站为例,验证模型在滤除光伏发电功率噪声和提升预测精度方面的有效性。A short-term photovoltaic power prediction model based on bi-directional long short-term memory(BiLSTM)considering data decomposition and evolutionary predation strategy is proposed.Firstly,for a large number of high-frequency components and complex frequency components of the original PV power,the SCS method(SVD-CEEMD-SVD,SCS)is developed to fuse the complementary ensemble empirical modal decomposition(CEEMD)with the singular value decomposition(SVD)of matrix operations through the data decomposition theory,which can realize the secondary noise reduction of PV power data.In addition,a combined prediction model with evolutionary predation strategy(EPPS)and BiLSTM is established to better exploit the intrinsic features of the proposed model for improving the prediction accuracy.Finally,the effectiveness of the model in filtering out the PV power noise and improving the prediction accuracy is verified by taking an actual PV power plant in a region of Shandong as an example.

关 键 词：光伏发电 预测 奇异值分解 进化捕食策略 双向长短期记忆网络 

分 类 号：TM715[电气工程—电力系统及自动化]