With the proposal of the dual-carbon economy,smart grids are developing in the direction of energy con-servation and emission reduction,and the abnormal power consumption of users has caused serious loss of power re-sources.Aiming at the problems of low accuracy and slow operation efficiency of traditional abnormal power con-sumption detection methods,a lightGBM model combined with an improved long short-term memory network model is proposed for abnormal power consumption detection.Anomaly detection is carried out by combining sampling and lightGBM model,and abnormal electricity consumption category is given by improving long short-term memory net-work model.The advantages of the proposed method are analyzed through experiments.The results show that,com-pared with traditional detection methods,the proposed method can detect abnormal users quickly and effectively,with a detection accuracy of 98.64％,meanwhile,the abnormal data is effectively classified,and the comprehen-sive classification accuracy rate is 96.60％,which provides a certain reference for the development of anomaly de-tection technology.

In order to improve the performance of feature extraction, this paper proposes a new retrieval model based on coupled feature extraction. In this paper, we preprocess the big data, process the big data by distributed fusion, analyze the characteristics of the big data, extract its statistical features, and construct the big data distribution structure model. By extracting the power user coupling feature, the multi-space memory distribution of user variable relation big data features extraction and retrieval is obtained, and the analysis model is optimized. Experimental results show that the retrieval model based on this method can improve the capability of retrieval and information access, which is helpful to improve the retrieval ability to a certain extent.

大数据、云计算和物联网等新技术在智能电网建设中的应用普及,使电力数据正在呈现出爆发式的增长,给电网企业的发展带来了很大的影响和冲击,成为企业内部进行优化和管理的重要契机,只有做好数据管理工作才能有效挖掘数据价值.在大数据时代当中,电网企业不仅需要对数据信息进行积极的掌握,同时也需要对大数据的应用方式进行重视,通过大数据的方式来实现市场信息的掌握和分析,为电网企业的发展提供更多的数据支撑.

国家电网在2019年初明确提出,要重点做好"泛在电力物联网"建设,促进电网与互联网的深度融合.针对北京2022年冬奥场馆的电力通信系统,本文将围绕奥运场馆电力通信需求、电力通信网整体架构以及详细的电力通信网感知层接入方案设计为奥运场馆电力通信网的组网提供可行、有效的方案,感知层是泛在电力物联网的基础,保障电力通信网的全息感知与泛在连接,能够为奥运场馆提供可靠、优质、稳定、绿色、智能的供电服务.

为了给全世界奉上一届绿色环保、精彩卓越的盛会,对于电力保障方面有很大的要求和责任.电力保障作为冬奥会举办不可或缺的条件,如何在地形复杂、冰天雪地的保电作业条件下,保障电力输送的稳定性,支撑冬奥会的顺利举办,对电力物资安全、及时输送、作业人员安全防护、特殊环境作业技能等供电保障需求都产生了更高的需求.本项目开展冰雪、山地环境下物资运载及保障人员作业装备等供电保障研究,实现冬奥会电力供给的可靠保障,支撑冬奥会的顺利开展.

Under the background of the further development of electric power, this paper forecasts the spatial load of distribution network, and proposes a multi-stage spatial load forecasting method considering the demand side resources. Firstly, the load of distribution network is pretreated to improve the prediction function of the processing system, and the working efficiency of the whole system is enhanced to solve the maximum load value. Then, the different conditions of demand side resources are considered step by step to realize the fine analysis, confirm the saturation density value of load, understand the specific information of spatial load, master the predicted data status, and finally carry out the comprehensive prediction method research of spatial load to realize the prediction research of spatial load of distribution network. The experimental results show that the multi-stage spatial load forecasting method considering demand side resources has high accuracy and reliability, and its forecasting effect can improve the system forecasting performance to a certain extent, reduce unnecessary operation time, reduce energy and resource consumption, and promote the development of load forecasting research.

该文提出了一种基于实测数据由GPRS数据传输、非量测节点由预测产生的完备的运行数据库的配电网运行管理系统。该系统为用户提供了友好的界面,实现配电系统的科学运行管理,达到降低供电成本、节约能源、提高供电电能质量和供电可靠性的目的。