Open AccessAnalysis of integrated energy‐load characteristics based on sparse clustering and compressed sensing
Author(s) -
Wang He,
Hou Yongshan,
Yu Huanan
Publication year - 2019
Publication title -
iet energy systems integration
Language(s) - English
Resource type - Journals
ISSN - 2516-8401
DOI - 10.1049/iet-esi.2018.0038
Subject(s) - compressed sensing , energy (signal processing) , cluster analysis , computer science , coupling (piping) , process (computing) , construct (python library) , efficient energy use , data mining , algorithm , engineering , artificial intelligence , mathematics , electrical engineering , statistics , mechanical engineering , programming language , operating system
An integrated energy system not only provides a platform for multi‐energy coupling utilisation but also satisfies users' diversified energy demands. However, in view of the enormous amount of integrated energy data and the difficulty of analysing the characteristics of that data, an integrated energy‐analysis method based on sparse clustering and compressed sensing is proposed in this study. This method uses the fuzzy c‐means algorithm to construct an over‐complete dictionary and then compresses, collects, and reconstructs the integrated energy data using the compressed sensing theory method. This process analyses integrated energy‐load characteristics accurately and also solves the problem of low data‐transmission efficiency. Simulation results show that the method is suitable for analysing and processing integrated load data in integrated energy systems.