Paper
16 December 2022 Intelligent control level assessment of distributed energy devices based on big data feature mining
Jian Xu, Qiao Sun, Weikang Kong, Ying Li, Junfei Wang, Dan Meng
Author Affiliations +
Proceedings Volume 12500, Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022); 1250054 (2022) https://doi.org/10.1117/12.2661157
Event: 5th International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022), 2022, Chongqing, China
Abstract
The development of distributed energy technologies has accelerated the cross-fertilization of different energy systems and has brought new challenges to the development of the energy internet and energy management. The core of current energy development is efficient energy use and low carbon environmental protection. To achieve efficient energy use, this paper analyzes the level of intelligent control of distributed energy devices based on big data feature mining technology to enhance the autonomy of individual energy supply and energy use and the synergy of the system to achieve the purpose of decentralization. However, the existing data processing work lacks data quality evaluation standards that are unified and deeply integrated with professional management, and the quality and validity of the stock and incremental basic data cannot be assessed comprehensively. To this end, we propose and build an evaluation system for intelligent control level of distributed energy equipment, and through the establishment of a data quality evaluation system to conduct comprehensive analysis and evaluation of distributed equipment basic data, we realize effective control of basic data status and make data governance work more intelligent.
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Jian Xu, Qiao Sun, Weikang Kong, Ying Li, Junfei Wang, and Dan Meng "Intelligent control level assessment of distributed energy devices based on big data feature mining", Proc. SPIE 12500, Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022), 1250054 (16 December 2022); https://doi.org/10.1117/12.2661157
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KEYWORDS
Data modeling

Solar energy

Control systems

Data storage

Data centers

Data acquisition

Information fusion

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