District-level algorithmic clustering for regional strategy developement in the heat transition

Extended abstract 5-194-26

Geographical classification plays a critical role in formulating strategic recommendations for heat planning by recognizing synergies among comparable and geographically proximate areas. This typically involves categorizing sub-areas according to similar attributes. However, this process is often labor-intensive and time-consuming, as it relies heavily on manual analysis and extensive stakeholder consultation. The complexity is further exacerbated by the politicized nature of regional planning, frequently necessitating multiple iterations of area delineation, thereby increasing the overall workload.

This abstract discusses a novel method for the initial grouping of sub-areas, using the German federal state of Saarland as a case study. The approach seeks to improve the speed, accuracy, and transparency of geographical classification, while also increasing ease of modification Each district within Saarland was evaluated based on a predetermined set of criteria, including the viability of heating networks, the presence of centralized and decentralized heating sources, and the respective heat demands of private and industrial sectors. The potential of centralized heat sources was normalized in relation to the identified grid connection potential and aggregated into a composite index.

Algorithmic clustering techniques were employed to implement the initial grouping of districts. Subsequent analysis of the resulting clusters allowed for the identification of logical boundaries, sensitivities, and discontinuities in the clustering criteria. An iterative process was then utilized to refine the clustering parameters, with the objective of achieving a coherent and logically consistent classification. The final geographical classification derived from this methodology provides a foundation for policy recommendations on municipal and state levels and facilitates the identification of synergies between similar and spatially adjacent areas.

While the method is, in principle, transferable to other regions, challenges may arise when applied to areas with substantial structural differences compared to Saarland.

Read the full abstract.

Download presentation.