检索结果-南通市图书馆

Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting

Digital Communications and Networks 2023年第5期9卷 1221-1229页

作者： Weishan Zhang Xiao Chen Ke He Leiming Chen Liang Xu Xiao Wang Su Yang College of Computer Science and Technology China University of Petroleum(East China)China Sichuan Energy Internet Research Institute Tsinghua UniversityChina School of Computer and Communication Engineering Beijing University of Science and TechnologyChina State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science Fudan UniversityChina

Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient training data and enough computational ***,there are challenges in building models through centralized shared data due to data privacy concerns and industry *** learning is a new distributed machine learning approach which enables training models across edge devices while data reside *** this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM *** design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting *** evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.

关键词： photovoltaic power forecasting Federated learning Edge computing CNN-LSTM

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

photovoltaic power forecasting based on weather classification using LSTM

Photovoltaic power forecasting based on weather classificati...

引用

第32届中国过程控制会议(CPCC2021)

作者： Lei Guo Xiangjie Liu Xiaobing Kong School of Control and Computer Engineering North China Electric Power University

In order to reduce possible negative impacts of photovoltaic(PV) power penetration into modern power grids,an accurate forecasting of PV power output is ***,photovoltaic power forecasting(PVPF) is a rather difficult challenge because of its highly varying and complex *** this article,a weather-based hybrid PVPF method is proposed,combining weather classification,long short-term memory network,and the fuzzy inference approach to make 5-minutes-ahead forecasting of PV power *** correlation coefficient is adopted as a feature selection *** effectiveness of the proposed model is evaluated by comparing with the multilayer perceptron neural network and traditional long short-term memory *** results show that the proposed model shows better forecasting accuracy.

关键词： photovoltaic power forecasting Weather classification Long short-term memory networks Fuzzy inference approach Pearson correlation coefficient

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Hybrid SDS and WPT-IBBO-DNM Based Model for Ultra-short Term photovoltaic Prediction

引用

CSEE Journal of power and Energy Systems 2023年第1期9卷 66-76页

作者： Hui Hwang Goh Qinwen Luo Dongdong Zhang Hui Liu Wei Dai Chee Shen Lim Tonni Agustiono Kurniawan Kai Chen Goh School of Electrical Engineering Guangxi UniversityNanningGuangxi 530004China University of Southampton Malaysia Iskandar Puteri 79200Malaysia College of the Environment and Ecology Xiamen UniversityFujian 361102China Department of Technology Management Faculty of Construction Management and BusinessUniversity Tun Hussein Onn Malaysia86400 Parit RajaJohorMalaysia

Accurate photovoltaic(PV)power prediction has been a subject of ongoing study in order to address grid stability concerns caused by PV output unpredictability and *** paper proposes an ultra-short-term hybrid photovoltaic power forecasting method based on a dendritic neural model(DNM)in this *** model is trained using improved biogeography-based optimization(IBBO),a technique that incorporates a domestication operation to increase the performance of classical biogeography-based optimization(BBO).To be more precise,a similar day selection(SDS)technique is presented for selecting the training set,and wavelet packet transform(WPT)is used to divide the input data into many *** is then used to train DNM weights and thresholds for each component ***,each component’s prediction results are stacked and *** suggested hybrid model is used to forecast PV power under various weather conditions using data from the Desert Knowledge Australia Solar Centre(DKASC)in Alice *** results indicate the proposed hybrid SDS and WPT-IBBO-DNM model has the lowest error of any of the benchmark models and hence has the potential to considerably enhance the accuracy of solar power forecasting(PVPF).

关键词： Dendritic neural model improved biogeography-based optimization photovoltaic power forecasting similar day selection wavelet packet transform

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：