  This paper proposes a learning-based model predictive control (MPC) approach
for the thermal control of a four-zone smart building. The objectives are to
minimize energy consumption and maintain the residents' comfort. The proposed
control scheme incorporates learning with the model-based control. The
occupancy profile in the building zones are estimated in a long-term horizon
through the artificial neural network (ANN), and this data is fed into the
model-based predictor to get the indoor temperature predictions. The Energy
Plus software is utilized as the actual dataset provider (weather data, indoor
temperature, energy consumption). The optimization problem, including the
actual and predicted data, is solved in each step of the simulation and the
input setpoint temperature for the heating/cooling system, is generated.
Comparing the results of the proposed approach with the conventional MPC
results proved the significantly better performance of the proposed method in
energy savings (40.56% less cooling power consumption and 16.73% less heating
power consumption), and residents' comfort.
