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

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.