Learning-based Token Scheduling

This is an enhancement of the earlier token-based scheduling method. Learning-based token scheduling is a hierarchical distributed algorithm that aims at achieving energy savings in HVAC operations without compromising human comfort. It captures the influences of occupancy and user interactions. The algorithm involves these 5 steps:

1) Learning: each zone is controlled using numerous zone modules, which first runs a learning algorithm to correct (if needed) the zone thermal model using an moving horizon estimation approach.

2) Token Requests: the main role of the zone module is to run a model predictive controller (MPC) using forecast information on weather, occupancy, and cooling demands plus sensor readings (temperature, thermostat, and occupancy sensors) to compute the minimal energy required without breaching user-defined comfort margins. The minimum cooling energy computed is called cooling energy token.

3) Coefficient of Performance (COP) Constraints: the centralized controller checks whether the increase in cooling energy to be supplied using tokens as the lower bound will benefit the COP of the chiller system, thereby increasing the energy efficiency and reducing maintenance cost. The COP constraints are computed in the central scheduler and provided as input to the the quadratically constrained quadratic program (QCQP) model based on token requests from zone modules.

4) Mass Flow Rate Constraints: the tokens are converted into mass flow rates and the corresponding constraints are calculated. The minimum number of tokens required by each zone is computed based on the mass flow rate constraints in the central scheduler using zone temperature profiles and token requests from zone modules.

5) Token Allocation: the algorithm modifies the supply mass flow rate allocation to all zones, such that the duct pressure constraints are satisfied. It also ensures that the minimum damper position constraints are not violated. The computation is reduced to QCQP