Automatic Identification and Evaluation of Building Clusters for Optimal District Heating Networks Phasing

Abstract

Considering future city changes when designing district heating networks can be tedious. This paper tackles this problem by automatically identifying potential future building clusters connections, and evaluating relevant financial and planning criteria. The methodology uses open GIS data for cluster identification and building heat demand estimation, and thermal-hydraulic models for simulating the network. On a neighbourhood scale, area characteristics such as total heat demand, distance to source, and road surface type mainly affect the decision of where to expand the network to. However, on a street scale, the sizing and location of the initial network mainly affect the network’s phasing.