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Missing interpolation model for wind power data based on the improved CEEMDAN method and generative adversarial interpolation network 被引量:3
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作者 Lingyun Zhao Zhuoyu Wang +4 位作者 Tingxi Chen Shuang Lv Chuan Yuan Xiaodong Shen Youbo Liu 《Global Energy Interconnection》 EI CSCD 2023年第5期517-529,共13页
Randomness and fluctuations in wind power output may cause changes in important parameters(e.g.,grid frequency and voltage),which in turn affect the stable operation of a power system.However,owing to external factors... Randomness and fluctuations in wind power output may cause changes in important parameters(e.g.,grid frequency and voltage),which in turn affect the stable operation of a power system.However,owing to external factors(such as weather),there are often various anomalies in wind power data,such as missing numerical values and unreasonable data.This significantly affects the accuracy of wind power generation predictions and operational decisions.Therefore,developing and applying reliable wind power interpolation methods is important for promoting the sustainable development of the wind power industry.In this study,the causes of abnormal data in wind power generation were first analyzed from a practical perspective.Second,an improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)method with a generative adversarial interpolation network(GAIN)network was proposed to preprocess wind power generation and interpolate missing wind power generation sub-components.Finally,a complete wind power generation time series was reconstructed.Compared to traditional methods,the proposed ICEEMDAN-GAIN combination interpolation model has a higher interpolation accuracy and can effectively reduce the error impact caused by wind power generation sequence fluctuations. 展开更多
关键词 Wind power data repair Complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN) Generative adversarial interpolation network(GAIN)
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IoT data cleaning techniques: A survey
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作者 Xiaoou Ding Hongzhi Wang +3 位作者 Genglong Li Haoxuan Li Yingze Li Yida Liu 《Intelligent and Converged Networks》 EI 2022年第4期325-339,共15页
Data cleaning is considered as an effective approach of improving data quality in order to help practitioners and researchers be devoted to downstream analysis and decision-making without worrying about data trustwort... Data cleaning is considered as an effective approach of improving data quality in order to help practitioners and researchers be devoted to downstream analysis and decision-making without worrying about data trustworthiness.This paper provides a systematic summary of the two main stages of data cleaning for Internet of Things(IoT)data with time series characteristics,including error data detection and data repairing.In respect to error data detection techniques,it categorizes an overview of quantitative data error detection methods for detecting single-point errors,continuous errors,and multidimensional time series data errors and qualitative data error detection methods for detecting rule-violating errors.Besides,it provides a detailed description of error data repairing techniques,involving statistics-based repairing,rule-based repairing,and human-involved repairing.We review the strengths and the limitations of the current data cleaning techniques under IoT data applications and conclude with an outlook on the future of IoT data cleaning. 展开更多
关键词 Internet of Things(IoT) data quality data cleaning error detection data repairing
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