Abstract
Enterprise energy consumption is not only the main component of national energy consumption, but also an important regulatory object of energy conservation and emission reduction. Aiming at the practical problem of difficult discrimination of enterprise energy consumption anomaly, an enterprise energy consumption anomaly judgment based on clustering algorithm was proposed. Firstly, XGBoost was used for feature selection, and the missing energy consumption data is filled based on the generated countermeasure network; Then, the enterprise energy consumption scenarios are divided by DBSCAN algorithm, and all scenarios are analyzed one by one; Finally, the optimal number of clusters of energy consumption data is determined by SSE-SC index comprehensive decision, and the enterprise energy consumption standard library is divided by K-means++ clustering algorithm; Establish an abnormal energy consumption early warning mechanism to monitor the energy consumption of enterprises in real time.
Project Supported by Science and Technology Project of State Grid Shandong Electric Power Company “Research on the key technologies for intelligent research and judgment of energy efficiency anomalies of multi type enterprises based on massive data mining” (5206002000QW).
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Wang, Z., Geng, J., Wang, Q., Zhang, H., Meng, W., Li, L. (2022). Research and Judgment of Enterprise Energy Consumption Anomaly Based on Massive Data Clustering Algorithm. In: Tian, Y., Ma, T., Khan, M.K., Sheng, V.S., Pan, Z. (eds) Big Data and Security. ICBDS 2021. Communications in Computer and Information Science, vol 1563. Springer, Singapore. https://doi.org/10.1007/978-981-19-0852-1_47
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DOI: https://doi.org/10.1007/978-981-19-0852-1_47
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