Model predictive control
Arkitech’s predictive models combined with advanced algorithms, real-time data and dynamic setpoint adjustments continuously optimises the system’ s settings to reduce energy consumption while maintaining comfort and operational efficiency.
BALANCING ENERGY EFFICIENCY AND COMFORT
The system ensures that energy savings are maximized without compromising passenger comfort. Energy savings often come at the expense of thermal comfort or vice versa. The objective of the setpoint optimization is to find an optimal balance where energy use is minimized without sacrificing occupant comfort. For instance, raising the setpoint temperature of a chiller might reduce energy consumption by the chiller but could increase the energy use of water pumps or cause discomfort if the space is inadequately cooled.
HOLISTIC SYSTEM OPTIMIZATION
The dynamic setpoint optimization considers the entire system rather than just optimizing individual components (e.g., the chiller or AHUs). For example, optimizing the chilled water temperature alone can lead to higher energy use by other components like pumps and fans. Therefore, it is crucial that the model takes a “systems-level” approach that includes chillers, pumps and AHUs and other equipment (such as VFDs).
MACHINE LEARNING AND MODEL PREDICTIVE CONTROL
By using a combination of AI and model predictive control we train a dynamic model of the system to predict future behaviour and optimizes control actions over a finite time horizon. This allows the system to learn and adapt to changing conditions (e.g., occupancy, weather) and anticipate the need for heating or cooling.
