Sensor-Free Occupancy Forecasting for Smart Buildings: A Wi-Fi Syslog Approach With Machine and Deep Learning

Accurate short-term occupancy forecasting is essential for smart building operations such as energy management, space utilization, safety, and facility planning. However, many existing solutions rely on dedicated sensors that increase deployment cost and operational complexity and limit scalability. This paper proposes a sensor-free occupancy forecasting framework that utilizes Wi-Fi syslog data already generated by enterprise networks. The study uses two real-world datasets derived from campus and office building Wi-Fi infrastructures and evaluates several machine learning models, including Random Forest, Decision Tree, Gradient Boosting, and a Long Short-Term Memory (LSTM) network, for multi-step forecasting at a 5-minute resolution. Experimental results show that Random Forest achieves the highest accuracy, with Coefficient of Determination (R2) values of up to 0.997 and consistently low mean absolute error (MAE) and root mean squared error (RMSE), while LSTM provides competitive performance for short and medium forecasting horizons. Extended horizon experiments show that LSTM-based forecasts stay reliable for look-ahead periods of up to 60 minutes, while longer horizons show increased sensitivity to temporal variability and pattern changes. We also show that using only a small number of features is adequate to achieve high prediction accuracy, which simplifies data preparation and supports real-time deployment. The evaluation also examines cross-zone and cross-building generalization and demonstrates that short-term adaptation enables robust deployment across heterogeneous environments with limited retraining overhead. The proposed framework is integrated into an interactive dashboard to support visualization and decision-making. Overall, the results indicate that Wi-Fi syslog-based occupancy forecasting is a practical, scalable, and privacy-preserving approach for smart building management.