Abstract
Occupancy-aware heating, ventilation, and air conditioning (HVAC) control offers the opportunity to reduce energy use without sacrificing thermal comfort. Residential HVAC systems often use manually-adjusted or constant setpoint temperatures, which heat and cool the house regardless of whether it is needed. By incorporating occupancy-awareness into HVAC control, heating and cooling can be used for only those time periods it is needed. Yet, bringing this technology to fruition is dependent on accurately predicting occupancy. Non-probabilistic prediction models offer an opportunity to use collected occupancy data to predict future occupancy profiles. Smart devices, such as a connected thermostat, which already include occupancy sensors, can be used to provide a continually growing collection of data that can then be harnessed for short-term occupancy prediction by compiling and creating a binary occupancy prediction. Real occupancy data from six homes located in Colorado is analyzed and investigated using this occupancy prediction model. Results show that non-probabilistic occupancy models in combination with occupancy sensors can be combined to provide a hybrid HVAC control with savings on average of 5.0% and without degradation of thermal comfort. Model predictive control provides further opportunities, with the ability to adjust the relative importance between thermal comfort and energy savings to achieve savings between 1% and 13.3% depending on the relative weighting between thermal comfort and energy savings. In all cases, occupancy prediction allows the opportunity for a more intelligent and optimized strategy to residential HVAC control.
Highlights
Introduction and BackgroundThe finite quantity of fossil fuels and the mounting concern of climate change makes reducing energy use a global necessity
States, heating, ventilation, and air conditioning (HVAC) systems account for 50% of all building energy consumption [2], while U.S homes alone are responsible for the use of approximately 4.7 quadrillion
While all of the control strategies reduced the total energy used during the simulation, the two pure predictive models had the largest energy savings potentials, with 10.9% and 9.6% savings, respectively
Summary
Introduction and BackgroundThe finite quantity of fossil fuels and the mounting concern of climate change makes reducing energy use a global necessity. Energies 2020, 13, 5396 consumption associated with residential heating and cooling has the potential to result in large energy savings when applied across the sector. Different technologies have been added to HVAC systems to improve temperature control and reduce energy use, one of which is occupancy-based control. This method controls the indoor temperature to provide thermal comfort only when the building is believed to be occupied, and turns the HVAC system off when it is vacant. This typically results in reduced energy use during unoccupied hours
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