A University Building Test Case for Occupancy-Based Building Automation

Abstract

Heating, ventilation and air-conditioning (HVAC) units in buildings form a system-of-subsystems entity that must be accurately integrated and controlled by the building automation system to ensure the occupants’ comfort with reduced energy consumption. As control of HVACs involves a standardized hierarchy of high-level set-point control and low-level Proportional-Integral-Derivative (PID) controls, there is a need for overcoming current control fragmentation without disrupting the standard hierarchy. In this work, we propose a model-based approach to achieve these goals. In particular: the set-point control is based on a predictive HVAC thermal model, and aims at optimizing thermal comfort with reduced energy consumption; the standard low-level PID controllers are auto-tuned based on simulations of the HVAC thermal model, and aims at good tracking of the set points. One benefit of such control structure is that the PID dynamics are included in the predictive optimization: in this way, we are able to account for tracking transients, which are particularly useful if the HVAC is switched on and off depending on occupancy patterns. Experimental and simulation validation via a three-room test case at the Delft University of Technology shows the potential for a high degree of comfort while also reducing energy consumption.

Mitigation Strategies for Overheating and High Carbon Dioxide Concentration within Institutional Buildings: A Case Study in Toronto, Canada

“…Erickson et al [31] noticed that most buildings are conditioned without predicting the actual number of occupants in the space to start corresponding adjustments to the HVAC system and have noted that occupancy prediction is useful for mitigating this issue. Several studies [11,39,40] investigated smart heating systems and HVAC control strategies that can adjust set-point temperature using occupancy prediction algorithms. In existing buildings, delayed action on the HVAC operation corresponding to the occupancy in the zone often leads to unnecessary over-conditioning and extra energy consumption.…”

“…Dong et al [41] claim that a 20% reduction in energy consumption can be achieved using proper MPC. The MPC used in Swaminathan et al's [40] study consists of an integrated control structure to track changing occupancy patterns. It also features a pre-cooling action that is implemented prior to occupancy increase.…”

A Review of Mathematical Models of Building Physics and Energy Technologies for Environmentally Friendly Integrated Energy Management Systems

“…The grey-box models are combinations of white-box and black-box models. They rely on a simplified relationship of a physical process, and the parameters are regressed based on measured data and have been applied to model single energy technology, such as condensing boilers [191], thermal zone and HVAC systems [192] and building physics [193]. A high order grey-box implementation can be found in [169]: a 7R-4C model was implemented for an office building with years of measurement data, and is able to be solved by the built-in solver in MATLAB.…”

Optimization-Based Data-Enabled Modeling Technique for HVAC Systems Components

“…The study found that measured EUIs for 50% of the buildings deviated by about 25% from the projected performance, with 30% significantly better and 25% significantly worse [21]. Since most of the recent buildings HVAC systems controlled with a BAS system not only to collect data but to ensure occupant comfort [22]. Therefore, there has been an increase in the number of actual building performance data.…”