A Template-Based Approach for Generating Modelica Models of Building Electrical Systems from Semantic Models

Building electrical systems are becoming increasingly complex as designers evaluate AC, DC, and hybrid distribution architectures, integrate distributed energy resources, and maintain alignment with evolving performance and reliability goals. Existing design tools are typically limited, non-interoperable, and unable to support continuous modeling across design phases, resulting in fragmented workflows and significant manual effort. This paper presents a template-based workflow that automates the generation of high-fidelity Modelica simulation models of building electrical systems from semantic models. The workflow supports both basic safety analysis and the power-flow simulation of AC, DC, and hybrid system architectures. A Python-based middleware (RDF2EMO) was developed to automate data extraction, template instantiation, and parametric model generation, enabling rapid and consistent iteration through schematic design, design development, and construction documentation phases. Verification of the middleware automation (RDF2EMO) using a reference medium-sized office building demonstrates that the generated Modelica model is internally consistent with the Building Information Model. A case study demonstrates how the workflow supports design decisions, including system architecture selection, equipment sizing impacts and optimization, and reliability analysis.