2017 ASHRAE Annual Conference

Model predictive control (MPC) is becoming a popular algorithm for building HVAC supervisory control. One type of MPC for HVAC supervisory control is EnergyPlus Model-based Predictive Control (EPMPC), where an EnergyPlus model is used in MPC algorithm to predict future building performance. EPMPC could reduce the development cost of MPC by reusing the EnergyPlus model that is often developed during the design phase of a project. However, MPC, especially EPMPC, is much more complex and computation-intensive compared to traditional HVAC control logic; also, it needs to constantly acquire updated forecast data as inputs for computation, such as weather forecast data and occupancy schedule forecast data. Therefore, implementation of MPC to real HVAC systems is difficult. In this paper, a software framework of MPC for HVAC supervisory control is developed to facilitate implementation of MPC.

Intelligent building management systems aim to maintain thermal comfort and reduce energy consumption by learning occupant’s habits and behaviors. A component of these systems is dynamic scheduling, which ensures the HVAC system is only in use when the building is occupied. This paper develops dynamic schedules of building occupancy for an office building in London and estimate the effectiveness through a building simulation.

ASHRAE Guideline 36 aims to standardize a set of best practices for HVAC control. This paper compares a single-duct VAV terminal reheat strategy developed and implemented at Appalachian State University with the dual maximum strategy included in Guideline 36. The two strategies are similar but they differ in the details of implementation. The Appalachian State strategy controls the reheat valve with the zone temperature. Discharge air temperatures are monitored and when that temperature reaches 90 F, the VAV damper modulates to cap the discharge air temperature at the 90 F setpoint. Using numerical analysis and actual performance data, this study looks at the implications of this variation and compare operational outcomes with those established for the dual maximum strategy outlined in Guideline 36 and other energy conservation standards.

Small and medium sized commercial buildings, such as retail stores, restaurants and factories, often utilize multiple roof top units (RTUs) to provide cooling and heating for open spaces. A conventional control approach for these buildings relies on local feedback control, where each unit is cycled on and off using its own thermostat. Because a thermostat operates regardless of the overall building’s behavior, the conventional control approach could result in unnecessary energy use and high electrical peak demand via poor coordination among the units. The control solution is not site-specific and provides reduced energy consumption and peak demand with low sensor requirements. This paper provides recent results of long-term performance of the RTU Coordinator at field sites for small/medium commercial buildings.