中国电力2024，Vol.57Issue(7)：74-80,7.DOI:10.11930/j.issn.1004-9649.202310049
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
Distributed Photovoltaic Power Interval Prediction Based on Spatio-Temporal Correlation Feature and B-LSTM Model
摘要
Abstract
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.关键词
分布式光伏/时空关联性/区间预测Key words
distributed photovoltaics/spatio-temporal correlation/interval prediction引用本文复制引用
王海军,居蓉蓉,董颖华..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[J].中国电力,2024,57(7):74-80,7.基金项目
江苏省自然科学基金资助项目(BK20210046) （BK20210046）
江苏省 333 项目(500RC33322003、5002023006-1) （500RC33322003、5002023006-1）
南京铁道职业技术学院"青蓝工程"(QLXJ202111) （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). （KYPT2023003）
