A Machine Learning-Based Temperature Control and Security Protection for Smart Buildings

With the advent of IoT technology, smart building management has been transformed, leading to significant improvements in energy efficiency and occupant comfort. Indoor room temperature control is crucial as it affects both building performance and occupant quality of life. Nevertheless, strin-gent cybersecurity measures are required due to the increasing susceptibility to cyber attacks with more IoT links in smart buildings. Identifying and managing unusual temperature readings is essential to keep the system running smoothly, efficiently, and safely. By integrating classical control methods such as PID with anomaly detection and LSTM modeling, this approach enables proactive anomaly identification and accurate temperature fore-casts, rendering sustainable and resilient living conditions. This integration optimizes resource usage and mitigates cyber risks. This paper presents a holistic method that combines PID control, LSTM forecasting, and anomaly detection for smart building applications. The proposed integrated approach successfully addresses aberrant temperature variations and enhances building performance, as shown through experimental validation.