Key points are not available for this paper at this time.
Buildings are a major cause of carbon emissions . The building sector is responsible for around 40 % of energy consumption and for about 30 % of CO 2 emissions. Building Energy Management Systems (BEMS) are crucial for enhancing energy efficiency, and energy flexibility, and mitigating the environmental impact of buildings, which account for a significant amount of global energy use and greenhouse gas emissions . This study addresses the limitations of traditional BEMS by proposing a cloud-based IoT-BEMS with an intuitive user interface and advanced machine learning algorithms for energy optimization. The system integrates demand-side management techniques, including two main principles, load shifting through demand response and energy efficiency, allowing users to control appliances without requiring technical expertise. The results demonstrate significant energy savings, particularly in water heater optimization, with an average reduction of 24.23 % in energy consumption. Additionally, the Proximal Policy Optimization (PPO) algorithm used for electric vehicle (EV) charging resulted in an average cost saving of 30.6 % by leveraging off-peak electricity rates. The platform’s real-time data processing and user-friendly interface make it a robust solution for residential energy management , effectively balancing energy savings with user comfort. This research underscores the potential of IoT and machine learning in revolutionizing building energy management, contributing to the Sustainable Development Goals .
Islam et al. (Tue,) studied this question.