Evaluation of Online Tool Data Management for Warehouse Management for Power Big Data

Zhixin Jing, Rui Fan, Wan-zhao Liu, Yan Shi, Fengjiu Yang
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Abstract

With the rapid development of China's data industry, power big data has gradually become the main object of national construction and innovation. Especially in the promotion of sensors and intelligent equipment and so on, more and more electric power data sources, the type characteristics shown more complex, the use of big data related to technology, the hidden data information, not only can improve the efficiency of the power system, can also provide effective basis for warehouse management. As the main management tool for power big data, power load prediction can guarantee the power system and power supply quality on the one hand, and can provide more effective information by warehouse management on the other hand, and the actual prediction results directly affect the accuracy of the whole system operation. Therefore, on the basis of understanding the development trend of power big data, this paper takes data mining technology as the core to improve and explore the power load prediction, so as to ensure the accuracy and effectiveness of online tools and data management of warehouse management.
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面向电力大数据仓库管理的在线工具数据管理评估
随着中国数据产业的快速发展,电力大数据逐渐成为国家建设和创新的主要对象。特别是在传感器和智能设备等的推广下,电力数据源越来越多,类型特征表现得越来越复杂,利用大数据相关技术,将数据信息隐藏起来,不仅可以提高电力系统的工作效率,还可以为仓库管理提供有效依据。电力负荷预测作为电力大数据的主要管理工具,一方面可以保障电力系统和供电质量,另一方面可以通过仓库管理提供更有效的信息,实际预测结果直接影响整个系统运行的准确性。因此,本文在了解电力大数据发展趋势的基础上,以数据挖掘技术为核心,对电力负荷预测进行改进和探索,以保证仓库管理在线工具和数据管理的准确性和有效性。
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