Low carbon operation optimisation strategies for heating, ventilation and air conditioning systems in office buildings

Abstract

Heating, ventilation, and air conditioning (HVAC) systems play a crucial role in production environments. Optimising the control strategy of an HVAC system is an effective approach to achieving energy savings and reducing emissions in a production environment. In existing optimal control models for HVAC systems, occupant behaviour has been incorporated as a key influencing factor. However, model predictive control algorithms have not introduced to examine its performance in optimising HVAC system operation. An optimisation strategy considering occupant (leaders and users of HVAC systems) behaviour (OSOB) is proposed based on model predictive control algorithm. The concept of environmentally specific transformational leadership is introduced to characterise the behaviours of leaders. Then the distinct decisions driven by double objectives (carbon emissions and electricity costs) can be explained. The OSOB was applied to a representative office building in Beijing. The findings indicate that incorporating occupant behaviour into algorithm can lead to a significant reduction in carbon emissions, up to 45%, as well as a decrease in electricity bills, up to 17%. This study not only incorporates occupant behaviour as a significant influencing factor, but also offers valuable insights for product manufacturers seeking to reduce the carbon emissions produced during building operations.