摘要
短期光伏发电功率预测是电站制定发电计划并进行调度的重要组成部分,有助于电力系统的动态稳定。针对光伏时序预测中存在的噪声干扰及单一模型预测效果不稳定等问题,该文提出一种基于改进型自适应白噪声的完全集合经验模态分解(improved complete ensemble empirical mode decomposition with adaptive noise,ICEEMDAN)的组合预测模型。首先,利用相关系数提取重要气象特征,并采用模糊C均值聚类(fuzzy C-means clustering,FCM)将原始数据集划分为晴天、晴转多云和雨天。其次,每种相似日以最后一天为待预测日,其余为历史训练集;利用ICEEMDAN将历史训练集分解成若干个较为规律的子序列,并用排列熵(permutation entropy,PE)对各子序列进行重构。最后,高频项采用由卷积神经网络(convolutional neural network,CNN)、(bidirectional gated recurrent unit,Bi GRU)双向门控循环单元与注意力机制组合而成的CNN-BiGRU-ATTENTION神经网络预测,低频项和趋势项采用最小二乘支持向量回归机(least squares support vector regression,LSSVR)进行预测,将预测结果叠加得到最终光伏发电功率预测值。通过实例验证:该文组合模型在不同天气条件下,可以解决单一模型预测精度低、预测效果不稳定等问题;相比其他模态分解,能够更精确地预测波动较大的局部特征。
A short-term forecast of photovoltaic power(PV)is an essential component of power plant generation planning and scheduling,contributing to the dynamic stability of the power system.To address noise interference and unstable single-model predictions in photovoltaic time series forecasting,this paper proposes a combined prediction model based on improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN).Firstly,important meteorological features are extracted using correlation coefficients,and the original dataset is divided into categories such as clear sky,clear-to-partly cloudy,and rainy using fuzzy C-means clustering(FCM).Next,for each similar day,the last day is the target prediction day,and the rest is historical training data.ICEEMDAN decomposes the historical training dataset into several more regular subsequences.These subsequences are then reconstructed using permutation entropy(PE).Finally,the CNN-BiGRU-ATTENTION neural network,which combines convolutional neural network(CNN),bidirectional gated recurrent unit(BiGRU),and attention mechanism,is used to predict the high-frequency,low-frequency terms,and trend terms predicted by least squares support vector regression(LSSVR),and the prediction results are superimposed to get the final Predicted value of PV.Through practical verification,this combined model effectively addresses issues such as low accuracy and unstable predictions under different weather conditions;Compared with other modal decompositions,it can more accurately predict the fluctuating local features.
作者
龙小慧
秦际赟
张青雷
段建国
LONG Xiaohui;QIN Jiyun;ZHANG Qinglei;DUAN Jianguo(Logistics Engineering College,Shanghai Maritime University,Pudong District,Shanghai 201306,China;China Institute of FTZ Supply Chain,Shanghai Maritime University,Pudong District,Shanghai 201306,China)
出处
《电网技术》
EI
CSCD
北大核心
2024年第7期2948-2957,I0087-I0088,共12页
Power System Technology
关键词
光伏发电
模态分解
相似日聚类
卷积神经网络
最小二乘支持向量回归机
注意力机制
PV power
mode decomposition
fuzzy c-means clustering
convolutional neural networks
least squares support vector machine regression
attention mechanism
