Smart Building
Smart buildings leverage AI and IoT to optimize energy efficiency, occupant comfort, and security. Current research emphasizes using machine learning, particularly deep reinforcement learning and deep learning models like transformers and autoencoders, to analyze sensor data from various building systems (HVAC, lighting, occupancy) for predictive modeling, anomaly detection, and automated control. This field is significant due to its potential to drastically reduce energy consumption and carbon emissions in buildings, a major contributor to global climate change, while simultaneously improving building operations and occupant experience.
Papers
Intelligent Energy Management with IoT Framework in Smart Cities Using Intelligent Analysis: An Application of Machine Learning Methods for Complex Networks and Systems
Maryam Nikpour, Parisa Behvand Yousefi, Hadi Jafarzadeh, Kasra Danesh, Roya Shomali, Ahmad Gholizadeh Lonbar, Mohsen Ahmadi
Sequence-to-Sequence Model with Transformer-based Attention Mechanism and Temporal Pooling for Non-Intrusive Load Monitoring
Mohammad Irani Azad, Roozbeh Rajabi, Abouzar Estebsari