  Model predictive control (MPC) can provide significant energy cost savings in
building operations in the form of energy-efficient control with better
occupant comfort, lower peak demand charges, and risk-free participation in
demand response. However, the engineering effort required to obtain
physics-based models of buildings is considered to be the biggest bottleneck in
making MPC scalable to real buildings. In this paper, we propose a data-driven
control algorithm based on neural networks to reduce this cost of model
identification. Our approach does not require building domain expertise or
retrofitting of existing heating and cooling systems. We validate our learning
and control algorithms on a two-story building with ten independently
controlled zones, located in Italy. We learn dynamical models of energy
consumption and zone temperatures with high accuracy and demonstrate energy
savings and better occupant comfort compared to the default system controller.
