Context
Milan’s utilities are shifting from reactive maintenance to data‑driven approaches to better manage asset health and system performance, leveraging dynamic failure rate modeling to support flexibility services. By integrating advanced AI technologies with heterogeneous data sources, the initiative aims to improve network operations.
Challenge
Challenges persist in improving the reliability and efficiency of Milan’s electricity grid due to limitations in existing predictive maintenance approaches.
AI Solution
AI-driven predictive maintenance solution that integrates multi-source data (asset, topology, faults, weather, and load) to enable dynamic failure modeling, supporting diagnostic planning, resilience strategy development, and real-time fault identification in grid operations.
Impact & evidence
The solution improved diagnostic efficiency and accuracy in identifying high‑risk network segments which have reduced unexpected failures and service interruptions, strengthening overall grid reliability and resilience. It enhances grid stability and flexibility through predictive maintenance and anomaly detection, while optimizing costs, improving forecasting and planning, and driving productivity gains.
Implementation / Adoption
Implementation and scalability of the solution require availability of detailed network asset and topology data, historical fault and outage records, as well as load and meteorological data, along with integration into maintenance planning systems and control room operations. A structured deployment approach involves integrating multi-source data, developing AI models for dynamic failure rate estimation, validating outcomes on the distribution network, and progressively embedding insights into maintenance planning and real-time operations in alignment with regulatory frameworks.
Data
Solution requires availability of network asset registry and topology data, along with historical fault and outage records to capture asset behavior and failure patterns, as well as access to granular load data by network section and relevant meteorological data to enable accurate, context-driven failure prediction and analysis.
Future enhancements
Future enhancements include scaling the solution to larger network areas beyond Milan while continuously refining AI-driven failure prediction models for improved accuracy. Further integration with flexibility services and enhanced decision-support capabilities for resilience planning and control room operations will strengthen overall grid performance and adaptability.