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。
Abstract
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
Key words
photovoltaic power /
forecasting /
singular value decomposition /
evolutionary predation strategy /
bidirectional long short-term memory network
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基金
国家自然科学基金（51837007）; 山东省重点研发计划（2019JZZY020804）
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