摘要
通过Copula理论对多元负荷之间以及多元负荷与天气因素之间的非线性相关性进行分析。基于分析结果构建模型样本集,通过核主成分分析(KPCA)对样本集进行降维解耦,采用广义回归神经网络(GRNN)模型对多元负荷进行预测,模型参数采用遗传算法进行优化。以北方某建筑级区域综合能源系统夏季多元负荷中的电负荷和冷负荷算例为例,与其他两种预测模型进行对比,验证结果表明,基于Copula理论与KPCA-GRNN结合的区域综合能源系统多元负荷短期预测模型具有较高的预测精度,该模型具有一定的应用价值。
The nonlinear correlation between multiple loads and between multiple loads and weather factors is analyzed by Copula theory. Based on the analysis results,the model sample set is constructed. The sample set is decomposed by KPCA. The GRNN model is used to predict the multiple load and the model parameters are optimized by genetic algorithm. Taking the calculation example of electrical load and cooling load in a building-grade regional integrated energy system in north China as an example,compared with the other two prediction models,the verification results show that the model in this paper has higher prediction accuracy and better application value.
作者
马建鹏
龚文杰
张智晟
MA Jian-peng;GONG Wen-jie;ZHANG Zhi-sheng(College of Electrical Engineering,Qingdao University,Qingdao 266071,China;State Grid Qingdao Electric Power Supply Company,Qingdao 266002,China)
出处
《电工电能新技术》
CSCD
北大核心
2020年第3期24-31,共8页
Advanced Technology of Electrical Engineering and Energy
基金
国家自然科学基金项目(51477078)。
关键词
区域综合能源系统
多元负荷预测
COPULA理论
核主成分分析
广义回归神经网络
regional integrated energy system
multiple load forecasting
Copula theory
kernel principal component analysis
generalized regression neural network