  Modern buildings encompass complex dynamics of multiple electrical,
mechanical, and control systems. One of the biggest hurdles in applying
conventional model-based optimization and control methods to building energy
management is the huge cost and effort of capturing diverse and temporally
correlated dynamics. Here we propose an alternative approach which is
model-free and data-driven. By utilizing high volume of data coming from
advanced sensors, we train a deep Recurrent Neural Networks (RNN) which could
accurately represent the operation's temporal dynamics of building complexes.
The trained network is then directly fitted into a constrained optimization
problem with finite horizons. By reformulating the constrained optimization as
an unconstrained optimization problem, we use iterative gradient descents
method with momentum to find optimal control inputs. Simulation results
demonstrate proposed method's improved performances over model-based approach
on both building system modeling and control.
