Hybrid LSTM-MLP model with NSGA-II-based hyperparameter optimization for non-invasive occupancy estimation

Understanding occupancy patterns in buildings is critical for optimizing energy use, improving indoor comfort, and enabling smarter building management systems. Traditional methods for occupancy detection often rely on dense networks of sensors or invasive technologies such as cameras or wearables, raising concerns about cost, scalability, and privacy. In contrast, data-driven approaches based on environmental and energy-related signals offer a promising alternative: they are cost-effective, unobtrusive, and compatible with existing infrastructure. This study presents a robust and generalizable deep learning framework for occupancy estimation using easily accessible data sources, including electricity consumption, CO 2 concentration, and working-hour schedules. Through an extensive comparative analysis of different input combinations in supervised model configurations, the optimal trade-off between accuracy and computational cost for real-time deployment is identified. The results highlight the value of selecting appropriate variables and reveal how models using minimal inputs can provide reliable estimations when properly designed. By demonstrating a non-invasive, privacy-preserving, and scalable approach to occupancy modeling, this work contributes to the development of energy-aware and intelligent buildings, essential for meeting sustainability goals and enhancing user-centric building automation. • Accurate occupancy prediction using non-intrusive, low-cost data sources. • Electricity consumption is a strong standalone predictor of human presence. • Adding CO 2 or schedules boosts accuracy in structured occupancy periods. • Models run efficiently and suit real-time, privacy-sensitive applications.