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Using AI to Optimize HVAC Is as Easy as Riding a Bike

[ Related: Why AI Will Become an Essential Business Tool ]

Below is a thermal model of a building that is probably the simplest HVAC simulation. This simulation can show how energy, the price of energy and peak demand can be reduced by optimizing the room temperature setpoint.

  • The model considers the whole building as a single zone, in which the building façade, the internal air, the furniture, etc., are always in thermal equilibrium.
  • The thermal loads on this zone are treated as signals that generate from stochastic processes.
  • The stochastic processes and the parameter values of the thermal model are chosen to correspond to a realistic building.
  • The HVAC power is the product of the absolute value of the HVAC thermal load and a cooling or heating factor.
  • The cooling factor changes with outdoor air temperature (OAT) in a threshold linear equation.

Like riding a bicycle

Controlling room temperature is almost like controlling speed when riding a bicycle. Considering the simple thermal model presented here, the dynamic equations of the two problems are almost identical.

[ Related: IIC Tests Approaches for Saving Energy in Cities ]

This room temperature “comfort range” is very different from the “deadband” commonly implemented in VAV boxes. The VAV box is the centerpiece of a Variable Air Volume (VAV) system that changes the air flow rate based on the local room temperature and, usually, a pair of setpoints. The “deadband” prevents the VAV damper from actuating all the time. A smart control algorithm can actively optimize the room temperature setpoint within the comfort range, but the actual room temperature is still free to wander within the “deadband” centered at the optimized setpoint at any point in time.

  1. Fixed at 22°C (71.6°F)
  2. Fixed at 23°C (73.4°F)
  3. Optimize between 21°C (69.8°F) to 23°C (73.4°F).

[ Related: How Virtual Power Plants Can Solve Grid Problems]

The simple simulation presented in this article is quite informative in testing and evaluating different room temperature optimization strategies, such as constrained optimization, dynamic programming and the more generic reinforcement learning. In the language of reinforcement learning, simulation provides an “environment” for the intelligent “agent” to interact with and train on. The simple simulation is like training level 1, which should be the easiest to pass.

Smart control strategies are cool (or hot)

Diagnostics help to make sure that the HVAC system can deliver a prescribed room temperature setpoint in the most efficient way. This step should come before room temperature optimization and continue to monitor the building throughout. Comfort voting application helps to adjust and widen the comfort range improving comfort, providing more ‘room’ for the optimization and hence saving more energy

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