A Systematic Approach for Exploring Tradeoffs in Predictive HVAC Control Systems for Buildings

TL;DR

This paper introduces a simulation framework to analyze tradeoffs between energy savings, occupant comfort, and predictive control accuracy in HVAC systems, validated on real-world building data.

Contribution

The authors develop a novel simulation framework that enables systematic exploration of tradeoffs in predictive HVAC control strategies using real occupancy data.

Findings

Predictive control can improve energy efficiency but may impact comfort.

Tradeoffs between control accuracy, energy savings, and occupant comfort are quantifiable.

Simulation results reveal optimal configurations for different stakeholder priorities.

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.