An Intelligent Data Processing Method for ResidentialLoad Analysis
Objective: is to solve the problems existing in residential load
data processing including massive amount data storage,
processing efficiency and effective analysis. Methods: we
proposed an overall processing architecture based on the
residential load data characteristics. The improved clustering and
associate rule mining algorithms have been fulfilled
parallelization calculated through cloud computing. Results: 1)
Residential electricity consumption patterns have been found out
2) The efficiency of our method has been varied compared with
the current system under different amount of data. Conclusion:
big data processing requirements are arising more in different
fields, our method would be helpful in grid construction as well
as the residential energy consuming modeling.
Keywords: cloud computing, residential load data, clustermodel, pattern recognition
Download Full-Text
ABOUT THE AUTHORS
Gaochao Cui
PhD student, will receive PhD degree in signal processing, information science, Saitama Institute of Technology, Japan, in 2017. He has published several papers related in signal processing and brain computer interface. Many conferences have invited him to give presentations to the international researchers. In the first year of PhD, he was selected as the member of Junior Research Associate program in RIKEN, Japan and worked out ICA toolbox with other colleagues.
Li Zhu
PhD student, will receive PhD degree in computer science, Cognitive Science, Xiamen University. Her research topics are related to data mining, signal processing and cloud computing. She has published several papers, one book and patent related to power system modeling, machine learning and big data processing.
Dongsheng Wang
master student, has interest in computer programming language and large-scale signal processing.
Jianting Cao
PhD, is the professor at Saitama Institute of Technology, Japan. His research interests in algorithms, machine learning and cloud computing.
Gaochao Cui
PhD student, will receive PhD degree in signal processing, information science, Saitama Institute of Technology, Japan, in 2017. He has published several papers related in signal processing and brain computer interface. Many conferences have invited him to give presentations to the international researchers. In the first year of PhD, he was selected as the member of Junior Research Associate program in RIKEN, Japan and worked out ICA toolbox with other colleagues.
Li Zhu
PhD student, will receive PhD degree in computer science, Cognitive Science, Xiamen University. Her research topics are related to data mining, signal processing and cloud computing. She has published several papers, one book and patent related to power system modeling, machine learning and big data processing.
Dongsheng Wang
master student, has interest in computer programming language and large-scale signal processing.
Jianting Cao
PhD, is the professor at Saitama Institute of Technology, Japan. His research interests in algorithms, machine learning and cloud computing.
