摘要
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。
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.
作者
李烁
陈新度
尹玲
张斐
吴鹏
赵松涛
Li Shuo;Chen Xindu;Yin Ling;Zhang Fei;Wu Peng;Zhao Songtao(School of Electromechanical Engineering,Guangdong University of Technology,Guangzhou 510000,China;School of mechanical engineering,Dongguan University of Technology,Dongguan 523000,China;Guangdong Intelligent Robotics Institute,Dongguan 523000,China)
出处
《太阳能学报》
EI
CAS
CSCD
北大核心
2022年第6期79-84,共6页
Acta Energiae Solaris Sinica
基金
广东省普通高校特色创新项目“面向大数据应用的车间装备智能化关键技术及应用开发”(2017KTSCX176)
广东省普通高校机器人与智能装备重点实验室(2017KSYS009)。
关键词
光伏发电
神经网络
主成分分析
数据分析
模型输入
功率预测
photovoltaic power generation
neural network
principal component analysis
data analysis
model inputs
power prediction
