为提升电力负荷预测数据采集的实时性与可靠性,针对采集过程存在的通信协议异构、传输延迟及网络安全风险等核心问题,系统研究计算机通信技术应用方案。提出基于协议转换网关的异构数据融合、基于光纤通信与服务质量(Quality of Service...为提升电力负荷预测数据采集的实时性与可靠性,针对采集过程存在的通信协议异构、传输延迟及网络安全风险等核心问题,系统研究计算机通信技术应用方案。提出基于协议转换网关的异构数据融合、基于光纤通信与服务质量(Quality of Service,QoS)调度结合的延迟优化,以及基于高级加密标准(Advanced Encryption Standard,AES)加密与安全散列算法(Secure Hash Algorithm,SHA)校验的安全防护策略。研究结果表明,计算机通信技术可有效解决力负荷预测数据采集过程中的多源数据互通瓶颈,显著降低传输时延,并增强数据传输的安全性。展开更多
With large-scale applications,the loss of power load data during transmission is inevitable.This paper proposes a data completion method considering the low rank property of the data.According to the low-rank property...With large-scale applications,the loss of power load data during transmission is inevitable.This paper proposes a data completion method considering the low rank property of the data.According to the low-rank property of data and numerical experiments,we find either the linear interpolation(LI)or the singular value decomposition(SVD)based method is superior to other methods depending on the smoothness of the data.We construct an index to measure the smoothness of data,and propose the SVDLI algorithm which adaptively selects different algorithms for data completion according to the index.Numerical simulations show that irrespective of the smoothness of data,the data complementing results of SVDLI are comparable to or better than the best of SVD or LI algorithms.The present study is verified using the measurements in China,and the public data of the Australian electricity distribution company and Lawrence Berkeley National Laboratory.展开更多
文摘With large-scale applications,the loss of power load data during transmission is inevitable.This paper proposes a data completion method considering the low rank property of the data.According to the low-rank property of data and numerical experiments,we find either the linear interpolation(LI)or the singular value decomposition(SVD)based method is superior to other methods depending on the smoothness of the data.We construct an index to measure the smoothness of data,and propose the SVDLI algorithm which adaptively selects different algorithms for data completion according to the index.Numerical simulations show that irrespective of the smoothness of data,the data complementing results of SVDLI are comparable to or better than the best of SVD or LI algorithms.The present study is verified using the measurements in China,and the public data of the Australian electricity distribution company and Lawrence Berkeley National Laboratory.
基金浙江省“尖兵”“领雁”研发攻关计划(2024C01058)浙江省“十四五”第二批本科省级教学改革备案项目(JGBA2024014)+2 种基金2025年01月批次教育部产学合作协同育人项目(2501270945)2024年度浙江大学本科“AI赋能”示范课程建设项目(24)浙江大学第一批AI For Education系列实证教学研究项目(202402)。
