Veynandt, FrancoisFrancoisVeynandtDerler, BernhardBernhardDerlerHeschl, ChristianChristianHeschl2025-04-012025-04-012024-06-01https://hdl.handle.net/20.500.11790/3899In the realm of building performance optimization, understanding occupancy dynamics is pivotal for enhancing both energy efficiency and occupant comfort. Occupancy forecasts, serving as critical inputs for data-driven predictive control technologies, play a significant role in this domain. To address this need, we propose a novel model that directly estimates building occupancy levels. This model is particularly applicable to buildings equipped with mechanical ventilation systems and CO2 concentration sensors. The number of persons is estimated by utilizing the CO2 production rate of people and applying the principle of mass conservation. The CO2-based approach has been validated with manually recorded ground-truth measurements. A forecast is generated using the first order Markov chain model in combination with an Agent-Based Modell (ABM). The probability transition matrix of the Markov chain defines the behaviour of the occupant-agents, which is used in the ABM to generate behaviour profiles. The model has been tested on four office rooms, with a one-year measurement dataset. The Markov chain with ABM provides a forecast, which encompasses the stochasticity of people's behaviour. The presence True Positive Rate (TPR) reaches 50 % and the False Positive Rate (FPR) is 15 %, in average. The occupancy TPR is only 30 % and the FPR 15 %. The proposed approach offers a framework to easily implement further variables, like occupancy-related power consumption, lighting operation, window opening etc.enWissenschaftlicher Artikel10.1088/1755-1315/1363/1/012094