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The proliferation of sensing technologies has allowed the collection of occupancy-related data to support various building applications, including adaptive HVAC and lighting controls, maintenance operations, and space utilisation. However, past occupancy prediction studies often considered different combinations of sensor data and investigated a limited number of space types. This study performs occupancy prediction based on a minimum sensing strategy by using a comprehensive set of sensor data (ie, indoor environmental and outdoor weather conditions, Wi-Fi connected devices, energy consumption data, HVAC operations, and time-related information) to identify the most crucial features through a proposed feature selection algorithm. Occupancy predictions were subsequently performed using different deep learning architectures, including Deep Neural Network (DNN), Long Short-Term Memory (LSTM …