Semantic integration for urban energy transition:Linked data for cross sector interaction

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:This dissertation provides a semantic framework for organizing heterogeneous urban energy data and demonstrates cross-sector analysis with PV and EV examples.

🏢実務担当者:Urban planners and energy utilities can use the neighbourhood ontology to link building and transport data for more integrated planning.

🏛政策担当者:Policymakers developing smart city or energy transition strategies can leverage the semantic integration approach to break data silos and enable cross-sector coordination.

Urban energy transition involves many urban domains and diverse forms of urban data. Data heterogeneity is persistent across sources, scales, and indicator definitions. Data silos remain common in both research and practice. These conditions limit transparent comparison across places and constrain collective planning across organisational boundaries. The dissertation addresses this challenge by examining: how semantic integration can enable the analysis of cross-sector interaction of building and transport energy systems in the context of urban energy transition? The research begins with a systematic review of district-level energy transition research. Using a multidimensional analysis, the review shows that the field has grown rapidly, but remains organised along separate technological, socio-political, governance, and market domains. This finding establishes the necessity for an integrative analytical foundation. On this basis, the dissertation proposes a neighbourhood-level semantic structure for organising urban energy evidence for integration and analysis. In doing so, it demonstrates that data organisation is not a neutral technical step, but part of the method by which urban energy problems are recognised and interpreted. Next, the dissertation applies this semantic approach in two cross-sector contexts. First, it extends the semantic approach to neighbourhood photovoltaic planning through the Neighbourhood Photovoltaic Generation Ontology, which links PV system information, neighbourhood and 3D building context, and dynamic time series inputs. The analysis estimates hourly photovoltaic generation and multiple roof coverage scenarios, showing temporal variation in local generation and photovoltaic potential across neighbourhoods. Second, the dissertation extends the approach to the integration of building and transport energy systems through Electric Vehicle Charging Activity, linking charging-related information, travel survey data, neighbourhood characteristics and photovoltaic capacity. The case shows a temporal mismatch between electric vehicle charging demand and photovoltaic generation, together with spatial differences in demand intensity and local supply-demand relations across neighbourhoods. These applications demonstrate that semantic integration can support measurable and planning-relevant analysis of interactions across building, transport, and local renewable energy systems. The study concludes that digital integration through linked data provides a reusable basis for cross-sector urban energy transition analysis. It makes three main contributions. First, it identifies sectoral separation and heterogeneous data organisation as key challenges to cross-sector urban energy analysis. Second, it develops a neighbourhood-scale semantic integration framework for organising and linking heterogeneous urban energy datasets. Third, it demonstrates the analytical value of this framework through empirical applications in photovoltaic generation assessment and electric vehicle charging analysis. Overall, the dissertation shows that semantic integration can move beyond domain-specific data management by enabling support for interaction analysis across datasets and urban sectors.

• openalex https://research.tue.nl/en/publications/d9fd4708-3fab-4403-a40d-d788aed1cd6efirst seen 2026-07-03 04:43:15