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<p>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.</p>
</abstract>

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.

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

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

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...

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

Online veröffentlicht: 30. Mai 2024

Seitenbereich: -

Eingereicht: 21. Feb. 2024

Akzeptiert: 05. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1219

Schlüsselwörter
Fitness function, Particle coding strategy, SN-PSO algorithm, Digital employees, Electric power statistics work

© 2024 Mingli Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

Online veröffentlicht: 30. Mai 2024

Seitenbereich: -

Eingereicht: 21. Feb. 2024

Akzeptiert: 05. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1219

SchlüsselwörterFitness function, Particle coding strategy, SN-PSO algorithm, Digital employees, Electric power statistics work

© 2024 Mingli Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Fitness function, Particle coding strategy, SN-PSO algorithm, Digital employees, Electric power statistics work