IoT-Enabled Demand Response Frameworks for Integrated Energy Management in Smart Buildings: A Systematic Review

This study conducted a systematic review of IoT-enabled demand-response frameworks for integrated energy management in smart buildings. Adhering to PRISMA 2020 guidelines, the review synthesises 51 peer-reviewed studies published between 2011 and 2026. The study examined IoT-driven improvements in demand-response efficiency, evaluated solar-aligned HVAC and EV scheduling to reduce fossil-fuel use, identified adoption barriers to IoT demand-response technologies, and compared dynamic IoT controls with static scheduling for peak loads. The literature indicates that automated, artificial intelligence- and machine learning-driven control strategies achieve total energy consumption reductions of 13–30%, peak-load reductions of 10–55%, and operational cost savings of up to 58%. These intelligent systems replace slow, manual interventions with closed-loop controls, significantly enhancing energy efficiency and grid stability. However, scaling these technologies faces critical barriers, including high upfront capital costs, infrastructure heterogeneity, cybersecurity vulnerabilities, and resistance to user behaviour. Furthermore, the review exposes a pronounced methodological gap: roughly two-thirds of the existing research corpus relies on theoretical models and simulations rather than large-scale, real-world empirical validation. To bridge the divide between simulated potential and field-proven performance, future investigations must prioritise an integrated research agenda encompassing multi-building district coordination, edge intelligence, digital twins, blockchain-enabled energy trading, and harmonised regulatory frameworks. Therefore, optimised IoT architectures are vital for advancing sustainable smart buildings, mitigating grid congestion, and achieving resilient, net-zero urban futures.