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Analysis of integrated energy‐load characteristics based on sparse clustering and compressed sensing

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.