## 1 Introduction  
Buildings consume a significant amount of energy globally, accounting for approximately 30% of total energy consumption and 25% of CO\({}_{2}\) emissions [1]. This highlights their critical role in climate change. Heating and cooling systems are the primary energy consumers in both residential (43%) and commercial buildings (30%) in developed countries [2]. Residential buildings account for nearly 70% of global building sector energy consumption, with thermal energy uses comprising a substantial and variable portion [3]. Over 68% of residential building energy is used for thermal purposes, including water heating (15.9%) and space heating (53.4%) [4]. The behavior of occupants, particularly in residential buildings, has attracted worldwide attention due to its potential to cause discrepancies between predicted and actual energy use and impact indoor thermal comfort [5]. Unlike other technical energy conservation measures, occupant behavior is uncertain and unpredictable, significantly affecting the development of strategic solutions for behavioral energy savings in residential buildings. While many researchers have primarily focused on analyzing and predicting window opening behavior by occupants, which significantly impacts indoor environments in residential buildings [6, 7, 8, 9, 10, 11, 12], there is increasing interest in estimating occupancy schedules and patterns to understand the relationship between space occupancy conditions, such as the number of occupants, duration and location of stay, random movements, and energy consumption in both residential and commercial buildings [13; 14; 15; 16; 17; 18; 19]. [PERSON] et al. conducted an extensive survey of 1628 households to investigate the relationship between occupancy conditions and energy consumption in residential buildings [20]. They found that the number of occupants is a significant variable in predicting daily maximum energy consumption but not daily minimum energy consumption. [PERSON] et al. presented a data-driven model that explored distinct occupant schedules and presence probabilities in a group of residential buildings. They identified lighting, plug load energy consumption, CO\({}_{2}\) levels, and relative humidity as indicators of occupant schedules and presence/absence [21]. [PERSON] et al. conducted experiments on human activity in residential buildings using real-time recognition of human activities (HAR) with an embedded algorithm for activity classification. They evaluated their developed algorithm with three different hardware architectures and found it to be a feasible solution for embedded systems [22]. The studies on occupancy models and interactions with heating and cooling systems have often focused primarily on the number of people in the space and their presence/absence, which may not be sufficient for deriving efficient control strategies for residential building heating and cooling devices. While these studies identified relationships between occupancy conditions and energy consumption, they did not fully explore the impact of individual occupant behavior and activities on energy use. Recognizing the significant influence of occupant behavior on energy performance in both residential and commercial buildings, researchers have become interested in developing optimal control strategies for heating and cooling systems. [PERSON] and [PERSON] conducted an energy survey of 36 residential units in Abu Dhabi, UAE, finding that air conditioning and domestic water heating systems operated continuously throughout the day, even during unoccupied times, in most rooms. By conducting parametric analysis using building energy modeling, they determined that overriding the room setpoint to 24 \({}^{\circ}\)C was the most effective energy-saving measure without major building retrofitting [23]. [PERSON] and [PERSON] pointed out that energy use in residential buildings is more impacted by occupant behavior and exhibits more irregular fluctuations compared to commercial buildings.