Abstract
In view of the continuous improvement of the current energy consumption data of many types of enterprises, the effective monitoring of enterprise energy consumption and the early warning of different reward energy use will be the top priority. This paper proposes a data-driven energy efficiency evaluation and energy anomaly detection method for multi-type enterprises based on energy consumption big data mining. This method uses K-means Clustering algorithm identifies the energy consumption patterns of different enterprises, which is convenient for the evaluation of enterprise energy efficiency. Then, on the basis of pattern division, the outlier detection of enterprise energy consumption data is completed by CEEMDAN-LOF algorithm, and the abnormal energy consumption detection and research of enterprises are realized. The example uses the real energy consumption data of power grid enterprises, and the simulation results show the effectiveness of the proposed method.
Project Supported by Science and Technologyleft-248285 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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References
Guo, Y., Zhang, X., Jiang, F., Zhang, J.: Research and management of water supply energy saving technology based on carbon peaking and carbon neutralization target. Water Supply Drainage 1–5 (2022). Accessed 28 Aug 2022
Zhang, B., Lan, C.: Research on the present situation and countermeasures of energy-conservation and emission-reduction in China’s iron and steel industry. In: 2010 Asia-Pacific Power and Energy Engineering Conference, pp. 1–4 (2010)
Zhang, J., Lei, Y.: Research on the multi-objective optimized model of power generation dispatching based on low-carbon economy and energy-conservation. In: The 27th Chinese Control and Decision Conference (2015 CCDC), pp. 1651–1655 (2015)
Tian, Y., Yu, Z., Zhao, N., Zhu, Y., Xia, R.: Optimized operation of multiple energy interconnection network based on energy utilization rate and global energy consumption ratio. In: 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2), pp. 1–6 (2018)
Liu, H., Li, Z., Cheng, Q., Yao, T., Yang, X., Hu, Y.: Construction of the evaluation index system of the regional integrated energy system compatible with the hierarchical structure of the energy Internet. In: 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2), pp. 342–348 (2020)
Jian, S., Li, W., Peng, X., Yan, Z., Cheng, C., Yuan, H.: Abnormal detection of power consumption based on a stacking ensemble model. In: 2021 4th International Conference on Energy, Electrical and Power Engineering (CEEPE), pp. 1021–1026 (2021)
Li, Z.: Abnormal energy consumption analysis based on big data mining technology. In: 2020 Asia Energy and Electrical Engineering Symposium (AEEES), pp. 64–68 (2020)
Han, Z., Zhang, C., Gao, M.: Hybrid energy storage planning for island-type integrated energy system based on EMD decomposition. Therm. Power Gener. 2022(09), 72–78 (2022). Accessed 28 Aug 2022
Arkhangelski, J., Abdou-Tankari, M., Lefebvre, G.: Efficient abnormal building consumption detection by deep learning LSTM IOT data classification. In: 2022 11th International Conference on Renewable Energy Research and Application (ICRERA), pp. 125–129 (2022)
Zhang, W., Dong, X., Li, H., Xu, J., Wang, D.: Unsupervised detection of abnormal electricity consumption behavior based on feature engineering. IEEE Access 8, 55483–55500 (2020)
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Liu, X., Geng, J., Wang, Z., Cai, L., Yin, F. (2023). Data-Driven Energy Efficiency Evaluation and Energy Anomaly Detection of Multi-type Enterprises Based on Energy Consumption Big Data Mining. In: Tian, Y., Ma, T., Jiang, Q., Liu, Q., Khan, M.K. (eds) Big Data and Security. ICBDS 2022. Communications in Computer and Information Science, vol 1796. Springer, Singapore. https://doi.org/10.1007/978-981-99-3300-6_1
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DOI: https://doi.org/10.1007/978-981-99-3300-6_1
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