Collaborative Optimization of Cloud–Edge–Terminal Distribution Networks Combined with Intelligent Integration Under the New Energy Situation

Fei Zhou (), Chunpeng Wu, Yue Wang, Qinghe Ye, Zhenying Tai, Haoyi Zhou and Qingyun Sun
Additional contact information
Fei Zhou: China Electric Power Research Institute Co., Ltd., Beijing 100192, China
Chunpeng Wu: China Electric Power Research Institute Co., Ltd., Beijing 100192, China
Yue Wang: China Electric Power Research Institute Co., Ltd., Beijing 100192, China
Qinghe Ye: China Electric Power Research Institute Co., Ltd., Beijing 100192, China
Zhenying Tai: Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing 100191, China
Haoyi Zhou: School of Computer Science and Engineering, Beihang University, Beijing 100191, China
Qingyun Sun: School of Computer Science and Engineering, Beihang University, Beijing 100191, China

Mathematics, 2025, vol. 13, issue 18, 1-22

Abstract: The complex electricity consumption situation on the customer side and large-scale wind and solar power generation have gradually shifted the traditional “source-follow-load” model in the power system towards the “source-load interaction” model. At present, the voltage regulation methods require excessive computing resources to accurately predict the fluctuating load under the new energy structure. However, with the development of artificial intelligence and cloud computing, more methods for processing big data have emerged. This paper proposes a new method for electricity consumption analysis that combines traditional mathematical statistics with machine learning to overcome the limitations of non-intrusive load detection methods and develop a distributed optimization of cloud–edge–device distribution networks based on electricity consumption. Aiming at problems such as overfitting and the demand for accurate short-term renewable power generation prediction, it is proposed to use the long short-term memory method to process time series data, and an improved algorithm is developed in combination with error feedback correction. The R 2 value of the coupling algorithm reaches 0.991, while the values of RMSE, MAPE and MAE are 1347.2, 5.36 and 199.4, respectively. Power prediction cannot completely eliminate errors. It is necessary to combine the consistency algorithm to construct the regulation strategy. Under the regulation strategy, stability can be achieved after 25 iterations, and the optimal regulation is obtained. Finally, the cloud–edge–device distributed coevolution model of the power grid is obtained to achieve the economy of power grid voltage control.

Keywords: cloud–edge–end distribution network; new situation of energy; artificial intelligence; distributed collaboration (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/18/2924/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/18/2924/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:18:p:2924-:d:1745978

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().