Article: Intelligent lifecycle management of distribution networks: a machine learning framework for efficiency, resilience, and environmental sustainability Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.22 pp.40 - 54 Abstract: Distribution network management has emerged as a crucial aspect of socio-economic survival and a driver of global technological change. This study proposes a machine learning (ML) lifecycle management framework that targets performance maximisation, system resilience enhancement, and the reduction of environmental pollution. Moreover, the latest ideas of data science, through advanced ML models are used in such areas as predictive maintenance, demand forecasting, fault detection, and energy flow optimization, which are key challenges addressed in this study. So, it results in lower operational costs, improved network reliability, and support of sustainability goals. The results showed that data science and network engineering should be combined in training programs to stimulate the sustainable development of new technologies. Most importantly, the research is a call for policy support and industry collaboration to speed up the use of smart systems in the formation of efficient, resilient distribution networks. Inderscience Publishers - linking academia, business and industry through research

Title: Intelligent lifecycle management of distribution networks: a machine learning framework for efficiency, resilience, and environmental sustainability

Authors: Xiaofeng Chen; Xiaomeng Zhai; Xiaohu Sun; Qian Hong; Jia Hu

Addresses: State Grid Economic and Technological Research Institute Co., Ltd., Beijing, 102200, China ' Economic and Technological Research Institute of Jiangsu Electric Power Co., Ltd., State Grid, Nanjing 210000, China ' State Grid Economic and Technological Research Institute Co., Ltd., Beijing, 102200, China ' State Grid Economic and Technological Research Institute Co., Ltd., Beijing, 102200, China ' State Grid Economic and Technological Research Institute Co., Ltd., Beijing, 102200, China

Abstract: Distribution network management has emerged as a crucial aspect of socio-economic survival and a driver of global technological change. This study proposes a machine learning (ML) lifecycle management framework that targets performance maximisation, system resilience enhancement, and the reduction of environmental pollution. Moreover, the latest ideas of data science, through advanced ML models are used in such areas as predictive maintenance, demand forecasting, fault detection, and energy flow optimization, which are key challenges addressed in this study. So, it results in lower operational costs, improved network reliability, and support of sustainability goals. The results showed that data science and network engineering should be combined in training programs to stimulate the sustainable development of new technologies. Most importantly, the research is a call for policy support and industry collaboration to speed up the use of smart systems in the formation of efficient, resilient distribution networks.

Keywords: machine learning; sustainable management; distribution networks; resilience; environmental protection.

DOI: 10.1504/IJICT.2025.146906

International Journal of Information and Communication Technology, 2025 Vol.26 No.22, pp.40 - 54

Received: 17 Mar 2025
Accepted: 16 Apr 2025
Published online: 25 Jun 2025 *

Title: Intelligent lifecycle management of distribution networks: a machine learning framework for efficiency, resilience, and environmental sustainability

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