考虑气象变化的光伏发电模型评估及研究

该文提出一种基于数据分析的发电模型评估方法,用于研究光伏发电模型输入,该方法主要由3 个步骤组成。首先,将基于信号分析的特征提取技术和基于专家知识的特征工程技术相结合扩展数据集,并进行异常值检测清除离群样本。其次对数据集进行相关性分析讨论输入数据的合理性。最后通过人工神经网络对该数据集进行建模,并把主成分分析引入模型训练中,分析模型在晴天、雨天、多云3 种不同气象条件下的表现。采用该方法对小型实验平台获取的气象数据与设备运行数据进行分析。实验表明,构造数据集比原始数据集训练的模型计算结果更精确,而引入主成分分析的模型计算效率更高。

EVALUATION AND RESEARCH OF PHOTOVOLTAIC POWER GENERATION MODEL CONSIDERING CLIMATE CHANGE

In this paper, a generation model evaluation method based on data analysis is proposed to study the PV generation model input. The method consists of three steps. Firstly, feature extraction based on signal analysis and feature engineering based on expert knowledge are combined to expand the data set, and outlier detection is performed to remove outlier samples. Secondly, the rationality of the input data is discussed through correlation analysis of the data set. Finally, the data set is modeled by artificial neural network, and principal component analysis is introduced into model training, and principal component analysis is introduced into the model training to analyze the performance of each model under three different meteorological conditions: sunny, rainy, and cloudy. Experiments show that the calculation result of the model trained by constructing the data set is more accurate than that trained by original data set, while the model introduced with principal component analysis is more efficient.