A short-term probabilistic photovoltaic power prediction method based on feature selection and improved LSTM neural network
A short-term probabilistic photovoltaic power prediction method based on feature selection and improved LSTM neural network
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DOI：
10.1016/j.epsr.2022.108069
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发表时间：
2022
影响因子：
3.9
通讯作者：
Ronghui Liu;Jiangchuan Wei;Gaiping Sun;S. Muyeen;Shunfu Lin;Fen Li
中科院分区：
文献类型：
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作者：
Ronghui Liu;Jiangchuan Wei;Gaiping Sun;S. Muyeen;Shunfu Lin;Fen Li
随着太阳能光伏在电力系统中渗透率的提高，光伏功率的随机波动对电网安全运行的影响越来越大。提出了一种有效的光伏预测方法，以准确地量化光伏系统发电量的变化性和不确定性。提出了一种基于云量和能见度的天气类型分类方法。提出了一种基于云量和能见度等气象数据的光伏发电预测模型，为了使模型表现出更好的性能，采用最大信息系数（MIC）选取特征变量。耦合输入遗忘门（CIFG）网络提出了最小化结构，而不显着降低预测精度。在此基础上，提出了一种结合分位数回归（QR）和CIFG网络的混合预测方法。然后，根据光伏输出的条件分位数，采用核密度估计（KDE）方法估计光伏输出的概率密度函数（PDF）。通过其他几种预测方法证明了所提出的预测模型的有效性和高可靠性，并观察到光伏发电预测的显着改善。
With the increase of solar photovoltaic(PV) penetration in power system, the impact of random fluctuation of PV power on the secure operation of power grid becomes more and more serious. An efficient PV forecasting approach is proposed to accurately quantify the variability and uncertainty of the power production from PV systems. This study proposes a classification method of weather types based on cloud cover and visibility. A PV power forecasting model is proposed, based on various meteorological data including cloud cover and visibility and in order to make the model show better performance, Maximal Information Coefficient(MIC) is used to select the feature variables. Coupled Input and Forget Gate(CIFG) network is proposed to minimize structure without significantly decreasing the prediction accuracy. Furthermore, a new hybrid method combining Quantile Regression(QR) and CIFG network is proposed to predict the conditional quantile of PV output. Afterward, Kernel Density Estimation(KDE) method is used to estimate PV output probabilistic density function(PDF) according to these conditional quantiles of PV output. The effectiveness and high reliability of the proposed forecasting model are demonstrated through several other forecasting methods, and a significant improvement in PV power prediction is observed.