Manufacturing systems, as significant energy consumers and potential contributors to energy efficiency optimization, play an important role in addressing global energy challenges. Digital Twins utilize available data from smart manufacturing systems to effectively understand and replicate systems' energy-related behaviors. Digital Twins facilitate detailed systems analysis and enable decision support for optimizing energy efficiency through performing relevant ‘what-if” scenario analyses. In this paper, we propose a methodology for data-driven extraction of simulation models for Energy-Oriented Digital Twins of smart manufacturing systems. Through a case study of a data-driven Energy-Oriented Digital Twin for an assembly process of a quadcopter drone part, we illustrate our initial methodology and the related data requirements. Our case study helps comprehend the complexity of extracting Energy-Oriented Digital Twins in smart manufacturing systems, offering insights into the integration of production and energy-related processes and behaviors of the system.