摘要
为解决传统预测算法的不足,利用深度信念网络(DBN)耦合支持向量机(SVM)和长短期记忆神经网络(LSTM),提出一种新的光伏功率组合预测方法.分别构建以高斯径向基函数为核函数的支持向量机预测模型、4层长短期记忆神经网络为单项预测模型,通过深度信念网络组合,优化预测结果并输出.根据实际出力和预测结果的误差,利用DBN动态调整以获得最优值,进一步验证SVM-LSTM-DBN模型的有效性和准确性,并以新疆维吾尔自治区某光伏电站的实测数据进行仿真验证.结果表明:基于SVM-LSTM-DBN组合的光伏出力预测模型与单一模型相比,预测精度明显提高.
In order to solve the shortcomings of traditional forecasting algorithms,a new combination prediction method of photovoltaic power is proposed by using deep belief network(DBN) coupled support vector machine(SVM) and long short-term memory neural network(LSTM).The support vector machine prediction model with the kernel function of Gaussian radial basis function and the 4-layer long-short-term memory neural network as a single prediction model.Through the combination of deep belief networks,the prediction results are optimized and output.According to the actual output and the error of the prediction results,the DBN is used for dynamic adjustment to obtain optimal value,to further verify the validity and accuracy of the SVMLSTM-DBN model.To take simulate and verify the actual measurement data of a photovoltaic power station in Xinjiang Uygur Autonomous Region.The results show that:compare the photovoltaic output prediction model based on the combination of SVM-LSTM-DBN and a single model,the prediction accuracy is significantly improved.
作者
卿会
郭军红
李薇
亢朋朋
王金明
潘张榕
QING Hui;GUO Junhong;LI Wei;KANG Pengpeng;WANG Jinming;PAN Zhangrong(College of Environmental Science and Engineering,North China Electric Power University,Beijing 102206,China;Key Laboratory of Resources and Environment System Optimization of Ministry of Education,North China Electric Power University,Beijing 102206,China;State Grid Xinjiang Electric Power Company Limited,Urumqi 830002,China;Altay Power Supply Company,State Grid Xinjiang Electric Power Company Limited,Altay 836500,China)
出处
《华侨大学学报（自然科学版）》
CAS
2022年第3期371-378,共8页
Journal of Huaqiao University(Natural Science)
基金
国家重点研发计划项目-战略性国际科技创新合作重点专项(2018YFE0208400)。
关键词
光伏发电
光伏出力预测模型
支持向量机
长短期记忆神经网络
深度信念网络
photovoltaic power generation
photovoltaic output prediction model
support vector machine
long and short-term memory neural network
deep belief network
作者简介
通信作者:李薇(1974-),女,教授,博士,博士生导师,主要从事能源环境污染控制、环境影响评价、环境规划与管理、节能减排优化、能源与环境系统分析等研究.E-mail:925657837@qq.com.
