Abstract
A novel effective method for identifying “Dispersed, Disordered, and Polluting” (DDP) sites was proposed for the purpose of promoting the modernization of ecological and environmental governance capabilities and building a big data application platform for enterprises’ pollution prevention. This paper aggregated data from the electricity consumption information collection system and characteristic sensing terminals, including user daily total electricity consumption records, peak and valley electricity consumption, and other information. Firstly, we used the hierarchical K-means algorithm to cluster the time series of user electricity consumption data. After ranking by cluster features, the electricity consumption time series of the selected suspicious users were encoded into Gramian angular field (GAF) images. Finally, we adopted the perceptual hash algorithm to build the model to identify “Dispersed, Disordered, and Polluting” sites. The case analysis results verified this method’s feasibility, rationality, and effectiveness.
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This research is sponsored by the Shandong Province Higher Educational Youth and Innovation Talent Introduction and Education Program.
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Zhang, X., Zhang, YF., Zhang, Y., Xiong, J. (2023). Image Intelligence-Assisted Time-Series Analysis Method for Identifying “Dispersed, Disordered, and Polluting” Sites Based on Power Consumption Data. In: Zhang, H., et al. International Conference on Neural Computing for Advanced Applications. NCAA 2023. Communications in Computer and Information Science, vol 1869. Springer, Singapore. https://doi.org/10.1007/978-981-99-5844-3_6
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