District heating network maintenance planning optimization

Abstract

To ensure the correct functioning of district heating networks and minimize critical failures, utilities allocate every year a significant part of their budget to maintenance operations. In the present work we describe a risk-based approach implemented to tackle the problem of designing optimal multi-year maintenance campaigns, applied to the Italian city of Brescia, showing how data-driven techniques can help decision makers assess the long terms impacts of budget allocations.

Keywords

• District heating network
• Maintenance planning
• Optimization
• Machine learning