Title: A Systematic Approach for Exploring Tradeoffs in Predictive HVAC Control Systems for Buildings,
Abstract: Heating, Ventilation, and Cooling (HVAC) systems are often the most
significant contributor to the energy usage, and the operational cost, of large
office buildings. Therefore, to understand the various factors affecting the
energy usage, and to optimize the operational efficiency of building HVAC
systems, energy analysts and architects often create simulations (e.g.,
EnergyPlus or DOE-2), of buildings prior to construction or renovation to
determine energy savings and quantify the Return-on-Investment (ROI). While
useful, these simulations usually use static HVAC control strategies such as
lowering room temperature at night, or reactive control based on simulated room
occupancy. Recently, advances have been made in HVAC control algorithms that
predict room occupancy. However, these algorithms depend on costly sensor
installations and the tradeoffs between predictive accuracy, energy savings,
comfort and expenses are not well understood. Current simulation frameworks do
not support easy analysis of these tradeoffs. Our contribution is a simulation
framework that can be used to explore this design space by generating objective
estimates of the energy savings and occupant comfort for different levels of
HVAC prediction and control performance. We validate our framework on a
real-world occupancy dataset spanning 6 months for 235 rooms in a large
university office building. Using the gold standard of energy use modeling and
simulation (Revit and Energy Plus), we compare the energy consumption and
occupant comfort in 29 independent simulations that explore our parameter
space. Our results highlight a number of potentially useful tradeoffs with
respect to energy savings, comfort, and algorithmic performance among
predictive, reactive, and static schedules, for a stakeholder of our building.