基于多元变分模态分解和混合深度神经网络的短期光伏功率预测

SHORT-TERM PHOTOVOLTAIC POWER FORECASTING BASED ON MULTIVARIATE VARIATIONAL MODE DECOMPOSITION AND HYBRID DEEP NEURAL NETWORK

针对传统分解预测方法忽略太阳辐照度等多维气象因素与光伏功率在时域和频域上的耦合关系以及深度神经网络在训练中出现的特征学习效率低、训练速度慢、过拟合等问题,提出基于多元变分模态分解(MVMD)和混合深度神经网络的短期光伏功率预测方法。首先,采用MVMD对光伏功率及多维气象序列进行时频同步分析,将其分解为频率对齐的多元本征模态函数,从而降低序列中非线性和波动性的影响。其次,针对多元本征模态函数,分别建立基于混合深度神经网络的预测模型。该模型采用卷积神经网络和双向长短时记忆神经网络来分别提取光伏功率及气象序列的空间相关特征和时间相关特征,并采用注意力机制来增强对重要时间点特征的学习权重。此外,使用残差连接来加快网络的训练速度以及缓解过拟合问题。通过实际工程实验分析,验证了该文方法的优越性。

In view of the traditional decomposition forecasting method neglecting the coupling relationship between multi-dimensional meteorological factors such as solar irradiance and photovoltaic power in time and frequency domains,as well as the low feature learning efficiency,slow training speed,over fitting and other problems in the training of depth neural network,a short-term photovoltaic power forecasting method based on multivariate variational mode decomposition(MVMD)and hybrid depth neural network is proposed.Firstly,MVMD is used to analyze the time-frequency synchronization of the photovoltaic power sequence and the multi-dimensional meteorological sequence,and decompose them into frequency-aligned multivariate intrinsic mode functions,thereby reducing the influence of nonlinearity and volatility in the sequence.Secondly,for the multivariate intrinsic mode functions,a forecasting model based on a hybrid deep neural network is established respectively.The model uses convolutional neural network and bidirectional long short-term memory neural network to extract the spatial correlation characteristics and temporal correlation characteristics of photovoltaic power and meteorological sequence,respectively,and uses attention mechanism to enhance the learning weight of important time point features.In addition,the residual connection is used to speed up the training speed of the network and alleviate the overfitting problem.The superiority of the proposed method in this paper is verified by the actual engineering experiment analysis.