Energy-Efficient Building HVAC Control Using Hybrid System LBMPC

Abstract

Improving the energy-efficiency of heating, ventilation, and air-conditioning (HVAC) systems has the potential to realize large economic and societal benefits. This paper concerns the system identification of a hybrid system model of a building-wide HVAC system and its subsequent control using a hybrid system formulation of learning-based model predictive control (LBMPC). Here, the learning refers to model updates to the hybrid system model that incorporate the heating effects due to occupancy, solar effects, outside air temperature (OAT), and equipment, in addition to integrator dynamics inherently present in low-level control. Though we make significant modeling simplifications, our corresponding controller that uses this model is able to experimentally achieve a large reduction in energy usage without any degradations in occupant comfort. It is in this way that we justify the modeling simplifications that we have made. We conclude by presenting results from experiments on our building HVAC testbed, which show an average of 1.5MWh of energy savings per day (p = 0.002) with a 95% confidence interval of 1.0MWh to 2.1MWh of energy savings.

Multiple time-scale model predictive control for thermal comfort in buildings

“…The air handling unit (AHU) supplies air at a specified temperature with an appropriate humidity level. The supply air temperature cannot be changed frequently because this can damage the HVAC components [2]. Thus, we assume that this value can be changed only once every hour.…”

Spot

“…It uses a simple reactive control strategy rather than a complex model-based predictive approach, such as the one in [7]. This is possible because personal heating and cooling systems such as radiant heaters and fans affect human comfort almost immediately 1 , unlike centralized HVAC systems, such as air conditioners and forced-air heaters, which can take tens of minutes to take effect.…”

“…Given these variables, it predicts the mean value of a group of people's votes in a 7-point ASHRAE [2] thermal sensation scale. The PMV model was first proposed by Fanger [15] in 1970 and it is widely used for evaluating thermal comfort [7] [20]. Although the model is based on a theoretically well-grounded physical thermal balance model, it has been found to be problematic to use in practice [18].…”

Zoned HVAC control via PDE-constrained optimization

“…Kelman and Borelli [3], as well as Hazyuk et al [4], [5], used low-dimension ODE-based models to control a HVAC unit using Model Predictive Control (MPC). Aswani et al [6] used a learning-based MPC algorithm [7] to account for unmodeled dynamics and disturbance in ODE models when controlling HVAC units. Domahidi et al [8] and Fux et al [9] also used a learning-based method and MPC, the first using ADABOOST to estimate uncertainties and the second using an Extended Kalman Filter.…”