Indoor occupancy estimation from carbon dioxide concentration using parameter estimation algorithms

The number of building occupants is an important indicator for predicting building energy consumption and developing control strategies for building automation. However, most occupancy estimation models were developed depending on the training steps where the true number of occupants is necessary. In order to calculate the occupant number independently, the newly-developed parameter estimation models were proposed, which are based on Maximum Likelihood (ML) approach and Bayesian analysis. A combination of multiple common measurements is used, including real-time CO2 concentration, energy consumption of facilities and make-up air system. The model starts by smoothing the raw CO2 concentration data using moving average, two-hour median as well as globally smooth. Then, ML and Bayesian analysis are used to establish the occupancy estimation models. The proposed models are evaluated in a commercial office which contains 36 occupants for validation. We find that the calculation errors could be reduced by using moving averaged data and globally smoothed data. The superiority of the parameter estimation models can be identified based on its lower calculation error and higher calculation accuracy compared to the previous established models. Practical Application Occupancy estimation models developed in this study are able to calculate occupant numbers independently and accurately in a non-intrusive way based on the indoor carbon dioxide concentration. This can provide input to a predictive building controller based on the application of occupancy estimation models. This could be applied to buildings across a district, informing demand-side management systems by employing occupancy behaviour and energy characteristics of individual buildings. This could allow both utility companies and building operators to simultaneously optimise their performance and benefit from this dedicated control strategy.