基于时空关联特征与B-LSTM模型的分布式光伏功率区间预测OA北大核心CSTPCD
Distributed Photovoltaic Power Interval Prediction Based on Spatio-Temporal Correlation Feature and B-LSTM Model
提出一种基于时空关联特征与贝叶斯-长短期记忆神经网络(bayesian long short-term memory,B-LSTM)模型的分布式光伏功率区间预测方法.以长短期记忆神经网络(long short-term memory,LSTM)为基础构建近似贝叶斯神经网络,建立考虑时空关联特征的B-LSTM模型,利用其强大的记忆能力和特征提取不同特征尺度的模态分量,并进行分布式光伏功率区间预测.以某地区实际分布式光伏数据集进行算例分析,验证了所提方法的优越性.
A distributed photovoltaic(PV)power interval prediction method based on spatio-temporal correlation features and bayesian long short-term memory(B-LSTM)model is proposed.The approximate Bayesian neural network is constructed by adding a Dropout layer based on the LSTM neural network to establish a B-LSTM model considering spatio-temporal correlation features,and its powerful memory and feature extraction capabilities are used to extract deep features for distributed PV power interval prediction for intrinsic mode function components with different feature scales.An arithmetic example is analysed with an actual distributed PV dataset in a region to verify the superiority of the proposed method.
王海军;居蓉蓉;董颖华
南京铁道职业技术学院,江苏南京 210031中国电力科学研究院有限公司,江苏南京 210003
分布式光伏时空关联性区间预测
distributed photovoltaicsspatio-temporal correlationinterval prediction
《中国电力》 2024 (007)
74-80 / 7
江苏省自然科学基金资助项目(BK20210046);江苏省 333 项目(500RC33322003、5002023006-1);南京铁道职业技术学院"青蓝工程"(QLXJ202111);南京铁道职业技术学院轨道交通基础设施智能检测研究中心科研平台资助项目(KYPT2023003). This work is supported by Natural Science Foundation of Jiangsu Province(No.BK20210046),the Sixth"333"Project of Jiangsu Province(No.500RC33322003,No.5002023006-1),Qing Lan Project of Nanjing Vocational Institute of Railway Technology(No.QLXJ202111),Funding for Scientific Research Platform for Railway Infrastructure Intelligent Inspection Research Centre of Nanjing Railway Vocational and Technical College(No.KYPT2023003).
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