Data-Driven Methodology for Energy and Peak Load Reduction of Residential HVAC Systems

Abstract

Residential buildings in the United States are responsible for the consumption of approximately 38% of electricity, and for much of the fluctuations in the power demands on the electric grid, particularly in hot climates. Residential Heating, Ventilation, and Air Conditioning (HVAC) systems are one of the largest electricity users of homes in these regions. “Smart” technologies, including electric grid-connected devices and home energy monitoring systems are increasingly available and installed in buildings, enabling new, data-driven methodologies for the operation of smarter, more sustainable building systems. This research investigates the use of residential energy use data and smart connected thermostat data to continuously monitor the health and performance of residential HVAC systems. Using field-collected HVAC energy consumption and performance data to develop a process-history based model, the results of this research suggest that the use of this methodology can save up to 6% of annual energy use of residential buildings.