Statistics and Analysis of Power Consumption Data Based on Big Data

Wan, Qingzhu; Yu, Yi; Wu, Kaicong; Li, Jiayao; Liu, Weina

doi:10.1109/icems.2019.8921894

Cited by 1 publication

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Technology in the information age is constantly moving forward, and the application of information technology and information systems has brought many new or old challenges to the statistical work of electric power enterprises, which at the same time puts forward new requirements for the work of electric power statistics [1][2][3]. The information age is more concerned with how to discover information, interpret information, and collect information together to produce new information and knowledge [4][5]. The faster information technology develops, the more statistical information is needed.…”

Section: Introductionmentioning

confidence: 99%

RPA-supported digital workforce in the field of statistical work in the electricity market

Li,

Cheng,

Luo

et al. 2024

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

How to count a large amount of data and information more efficiently in electric power statistics has become the main problem that electric power enterprises should pay attention to. For this reason, this paper proposes a study on the application of digital employees using RPA technology in the statistical analysis of the electric power market. Optimizing power market statistics is achieved by using the improved particle swarm algorithm SN-PSO, which is based on the fitness function and particle coding strategy to address the unbalanced load phenomenon. In AI-related technology, we create digital employees and conduct case studies on electricity market statistics by combining RPA technology with image recognition technology, NLP, and knowledge graph technology. The results show that the customers of the power supply company that show negative growth in electricity are mainly concentrated in the manufacturing industry (average value of 21.44) and the mining industry (average value of 7.36). In contrast, from the perspective of regional distribution, the negative growth rate of the customers in the large customers is mainly concentrated in the region of E and the remaining region of the large customers, which indicates that these two regions need to strengthen tracking management. This study improves the statistical work mode of electrical enterprises in the information age so that electric power enterprises and China’s market economy can develop better.

show abstract