Automated District-Level Energy Demand Modeling Using EnergyPlus Empowered by Digital Twin Technology

Abstract. The global shift toward decentralization and decarbonization in the energy sector demands robust tools for accurately simulating building energy demand at the district scale. Although EnergyPlus is well-regarded for its detailed building-level modeling, it poses challenges when extended to larger districts with diverse building typologies. This paper presents both a conceptual architecture and a working prototype of an automated pipeline that addresses these limitations. Leveraging open-access geo-spatial and non-spatial data from the Netherlands—including over 10 million buildings—the pipeline seamlessly scales EnergyPlus simulations to the district level. The system employs the OGC 3D Tiles standard for efficient streaming and real-time visualization of simulation results across a nationwide 3D Tileset. Implemented in Python and tested on local machines, the pipeline is poised for cloud-based deployment to further enhance scalability and performance. By integrating a digital twin for real-time monitoring and scenario testing, the approach enables efficient bulk operations, interactive decision support, and clearer insights into urban-scale energy consumption patterns. The resulting automation and scalable workflows offer valuable contributions to sustainable urban planning, ensuring that efficiency opportunities can be quickly identified and acted upon at multiple spatial scales.