Using neural network in a model-based predictive control loop to enhance energy performance of buildings

Abstract

Today’s environmental, energy and economic challenges require changes in the control strategies of HVACs’ systems, since they account for more than 60% of the building energy consumption. An optimal control law should be applied to reduce this consumption. To achieve this goal, a detailed description of the building is required, its construction components as well as the description of the occupants’ activities. A good knowledge of all building components is essential, especially the thermal characteristics of the building envelope, to build an energy model of the building. The dynamic thermal simulation tool, as EnergyPlus in this case, allows us to set up a huge database of the thermal behavior of our architectural model. This database was used for the training of an Artificial Neural Network model for modeling the thermal behavior of the building, with the aim of controlling the thermal comfort of the occupants, which means maintaining the temperature of the room within a setpoint temperature range. The developed control method, introduced in this work, reduced the energy consumption for cooling and heating, respectively, from 11.834 kWh to 9.025 kWh (27.34%) and from 4.631 kWh to 2.824 kWh (39.02%), compared to the On/Off control, during the 1 day of simulation.

Keywords

• MPC
• ANN
• Modeling
• Control
• Building energy efficiency
• IoT