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.

Reference building models can serve as a common baseline for research investigations and evaluations of design choices. Despite lighting being one of the largest single users of electricity in commercial buildings, lighting systems in reference buildings are commonly modeled as maximum building-type or space-type power densities defined in building energy codes. Further, energy use is typically estimated by modulating the lighting power over the course of a day via simple occupancy schedules that may not accurately portray human behavior or the performance of occupancy-based lighting controls. While this approach is suitable for some use cases, it is not adequate for exploring the impact of luminaire selection, space-specific control strategies, and occupant behavior. This paper presents the development of a high-fidelity reference model of a medium office building that includes interior architecture, an occupant distribution model, and a detailed lighting design intended to support more realistic evaluations of lighting control strategies and approaches to modeling occupant behavior. The model includes 13 market-representative LED luminaires, meets occupant lighting needs by complying with applicable recommended practices defined by the Illuminating Engineering Society (IES), and meets energy code power density and control strategy requirements in ANSI/ASHRAE/IES 90.1-2019. The luminaire-level connected load (26,037 W) of the lighting layout is lower than what would be estimated by simple building-type (34,304 W) and space-type (31,200 W) power density methods. The model includes 12 space types and 107 rooms with workstations for 268 occupants. The interior architecture varies on each of the three floors to introduce room and space-type diversity that further exposes the impact of design choices and occupant behavior. The model has already been used as a baseline for research on circadian lighting design strategies, lighting–HVAC data integration, and whole-building life-cycle assessments—demonstrating its utility as a common reference for a range of lighting research.