Abstract
In the existing building stock, heating, cooling and ventilation often run on fixed schedules assuming maximal occupancy. However, fitting the control of the HVAC system to the building’s real demand offers large potential for energy savings over the status quo. Building occupants’ presence as well as mechanically supplied and infiltrated airflow rates provide information that enables to define tailored strategies for demand-controlled ventilation. Hence, real-time estimations of these quantities are a valuable input to demand-controlled built environments. In this work, the use of stochastic differential equations (SDE) to estimate the room occupancy, infiltration air-rate and ventilation air-rate is investigated. In particular, a grey-box model based on a carbon dioxide (CO2) mass balance equation is presented. The model combines knowledge about the physical system with statistical, data-driven parameter estimation. Furthermore, the proposed model contains uncertainty parameters. This is in contrast to purely deterministic models based on ordinary differential equations, where uncertainty is usually disregarded. The suggested model has been tested in a naturally ventilated and in a mechanically ventilated environment; the performance in these two cases has been compared. We show that the ability to address measurement errors and non-homogeneous conditions in the room air implies that the suggested SDE-based grey-box approach is suitable in the context of demand-controlled ventilation.
Highlights
Heating, cooling and ventilation in buildings usually run on fixed schedules, in many cases even constantly throughout the day, all days
In contrast to earlier studies, that use ordinary differential equations (ODE) to describe the mass balance, the presented approach employs a greybox model based on stochastic differential equations (SDE)
A model which describes the variation in room CO2 level and can estimate room occupancy was presented
Summary
Heating, cooling and ventilation in buildings usually run on fixed schedules, in many cases even constantly throughout the day, all days. Reducing operation hours and airflow rates to the required extent enables potential energy-savings For this reason, reliable room occupancy estimates are needed to provide valuable information for an energy-efficient operation. The model estimates room occupancy based on a carbon dioxide (CO2) mass balance equation. It is possible to address and quantify the uncertainty that derives from measurement errors as well as from inadequacies in the description of the physical system The latter may concern the assumption of a homogeneously distributed pollutant concentration in the room air, the assumption of a constant infiltration rate and other oversimplifications in the model description. An introduction to the employed grey-box model is followed by a description of the occupancy estimation algorithm.
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