A Distributed ADMM-based Deep Learning Approach for Thermal Control in
  Multi-Zone Buildings
Vincent Taboga Hanane Dagdougui
The surge in electricity use, coupled with the dependency on intermittent
renewable energy sources, poses significant hurdles to effectively managing
power grids, particularly during times of peak demand. Demand Response programs
and energy conservation measures are essential to operate energy grids while
ensuring a responsible use of our resources This research combines distributed
optimization using ADMM with Deep Learning models to plan indoor temperature
setpoints effectively. A two-layer hierarchical structure is used, with a
central building coordinator at the upper layer and local controllers at the
thermal zone layer. The coordinator must limit the building's maximum power by
translating the building's total power to local power targets for each zone.
Local controllers can modify the temperature setpoints to meet the local power
targets. The resulting control algorithm, called Distributed Planning Networks,
is designed to be both adaptable and scalable to many types of buildings,
tackling two of the main challenges in the development of such systems. The
proposed approach is tested on an 18-zone building modeled in EnergyPlus. The
algorithm successfully manages Demand Response peak events.