Linked Geodata: Improving Rooftop Photovoltaic Production Estimates through BIM-GIS Integration using Semantic Web Technologies

英语. Accurately predicting rooftop photovoltaic (PV) electricity output is crucial for sustainable urban energy planning, yet current workflows that rely on 3D city models in CityGML Level of Detail 2 (LOD2) tend to inflate roof areas and, consequently, PV potential. This study develops and evaluates a proof-of-concept framework that integrates detailed Building Information Modelling (BIM) data in Industry Foundation Classes (IFC) format, with Geographic Information Systems (GIS) based 3D city models in CityGML format. It uses Semantic Web technologies to achieve interoperability. The approach enriches a CityGML knowledge graph (KG) by transferring precise roof area value from an IFC KG. The CityGML model for the test building, KTH Undervisningshuset, Stockholm, is stored in a relational database (3DCityDB), exposed as a virtual KG using R2RML mappings with Ontop plug-in in Protégé, and then materialized. The IFC model is converted to an IFC KG with the IFCtoRDF tool. A custom alignment ontology links equivalent IFC and CityGML classes and properties, enabling inference and the transfer of roof area data to the CityGML KG through SPARQL queries executed in GraphDB graph database. Using the IFC-derived roof area within the enriched CityGML KG reduces the estimated yearly PV electricity production by 16% (28.7 MWh/year) compared with the native CityGML LOD2 value, thereby eliminating the overestimation caused by geometric generalization. Benchmarking against the Photovoltaic Geographic Information System (PVGIS) shows that the knowledge graph-based estimate is 27% lower than the CityGML result and 12% lower than the IFC result. Although demonstrated on a single building and older schema versions (IFC2X3 TC1 and CityGML 2.0), the findings indicated that the method could reduce errors in PV estimates and support better decision-making in urban energy planning. As more IFC models become available, the workflow can be scaled at neighborhood and city levels, making Semantic Web technologies a viable alternative for BIM-GIS integration.

英语. Rooftop solar panels could supply a large share of our clean energy needs, but only if we know how much electrical power those roofs can deliver. That number depends on sunlight, the size of each roof, how efficient the panels are, and losses in the electrical system. Traditional methods often rely on simplified 3D city models, which often present roofs as simple surfaces, inflating their area and overestimating solar panel electricity production. This study explores how linking two models from different domains, a detailed building model (BIM) and 3D city model (GIS), can deliver more realistic predictions. Linking BIM and GIS Buildings are represented differently in BIM (typically used in architecture) and GIS (typically used in urban planning). BIM captures building details, whereas GIS focuses on building’s location and visualization. A proof-of-concept workflow is developed that links the two representations with Semantic Web technologies, which links data across different domains. This integration transfers precise rooftop area from the BIM to the GIS model, ensuring that urban planners get the best of both worlds and enabling more accurate calculations of solar panel energy production for sustainable energy planning. Results Testing this approach on a university building (KTH Undervisningshuset) in Stockholm revealed several key findings. First, the two building models were successfully converted into well-structured data networks and linked. Second, the BIM model showed a 16% smaller usable roof area (806 m2 vs 964 m2) than the traditional 3D city models used in GIS. That difference would overestimate yearly solar panel output by approximately 29 MWh, about the electricity consumed by 15 small apartments. Finally, to incorporate an alternative method and provide a more comprehensive assessment, results were benchmarked against the EU’s PVGIS tool for estimating solar panel energy production. The PVGIS estimate was approximately 27% lower than the GIS estimate and 12% lower than the BIM estimate. Application Accurate solar panel energy production estimates are vital for a city’s sustainable urban energy planning. This method helps urban planners prioritize rooftops with the highest potential, allocate resources efficiently, and avoid costly miscalculations. As more detailed BIM models become available, this approach can continue to enrich GIS 3D city models, allowing the analysis to scale to city level and supporting smarter decision-making process. Linking the BIM and GIS domains through open Semantic Web standards therefore offers a practical path towards a more reliable, scalable way to give planners, municipalities, and policymakers a clearer picture of urban renewable energy potential.