ELEVATING ENERGY EFFICIENCY: IDENTIFYING KEY TECHNOLOGIES FOR THE AUTONOMOUS LEVEL IN A FIVE-STAGE DIGITAL TWIN MATURITY MODEL

Digital Twins (DTs) , virtual representations of physical assets updated with real-time data, offer significant potential for enhancing energy efficiency through predictive analytics and automated control. In the Architecture, Engineering, Construction, and Operations (AECO) sector, adoption is limited by heterogeneous workflows and the absence of standardized, energy-focused maturity frameworks. This study aims to standardize DT workflows for energy efficiency by synthesizing insights from five existing models and categorizing DT capabilities into five levels: Digital Shadow, Digital Twin, Predictive Digital Twin, Autonomous Digital Twin, and Federated Digital Twin. A qualitative three-stage methodology—theoretical, analytical, and inductive approaches—involved a literature review, comparative model assessment, and case study analysis to validate the autonomous level. Four case studies demonstrating closed-loop control, self-optimization, and measurable energy savings were analyzed to identify essential tools, technologies, and workflows. The resulting framework provides AECO stakeholders with a structured roadmap for consistent DT adoption and improved energy performance in building energy management.