Leveraging IoT, digital twin and machine learning for smart energy audit in office building: a systematic literature review and recommendations

Here are concise and clear highlights capturing the key novel contributions and findings of our research:. • Existing energy audit methods largely utilize IoT, machine learning (ML), and digital twin (DT) technologies in isolation, resulting in fragmented system-level insights. • State-of-the-art studies indicate limited integration of occupant behavior and dynamic energy baseline modeling, reducing the practical effectiveness of current energy audits. • A novel conceptual framework is proposed, integrating IoT for real-time monitoring, ML for predictive analytics, and DT for optional scenario simulations, aligned with ISO 50000 standards. • The proposed AIoT-based audit framework enables continuous, system-level energy audits, providing actionable real-time efficiency recommendations for building management. • Future research should prioritize validating the integrated framework in operational settings, enhancing input parameters, and automating interactions with building management systems for scalable energy efficiency solutions. Energy audits play a pivotal role in improving energy efficiency and reducing carbon emissions in office buildings. However, conventional audits often suffer from fragmented insights, lack of system-level monitoring, establishing energy baseline, and insufficient incorporation of occupant behavior. To address these challenges, this study conducts a systematic literature review of recent applications of Internet of Things (IoT), machine learning (ML), and digital twin (DT) technologies in the energy audit domain. The review, guided by PRISMA methodology, analyzes eleven selected studies published between 2022 and 2024, revealing that while ML dominates in predictive modeling, IoT and DT remain underutilized in delivering integrated, efficiency recommendations. The analysis identifies three key engineering gaps: limited use of occupant behavior data, absence of continuous energy baseline modeling, and lack of systems capable of generating real-time efficiency recommendations. In response, this paper proposes a novel AIoT-based energy audit framework that combines real-time monitoring via IoT with ML-driven analytics and optimization, supported optionally by DT-based simulation. The proposed framework aims to enable continuous, system-level audits aligned with ISO 50000 standards, offering practical pathways for building managers to diagnose inefficiencies and implement energy-saving actions. Validating the model in real-world office environments, expanding input variables, and integration strategy with building automation systems are further important study to realize intelligent and scalable energy audit solutions.