Building operation consumes approximately 40% of energy in the United States, approximately half of which is consumed by residential buildings. Compared to commercial buildings where the push for commissioning in recent years has contributed to improved efficiency, less is known about the consumption of residential buildings, limiting the possibilities for performance-based analysis of these buildings. The increasing deployment of technologies such as smart meters and home energy management systems (HEMS) provide a great opportunity to better evaluate the performance of residential HVAC systems, one of the single greatest consumers of electricity in residential buildings, particularly in warm climates. The increasing availability of energy use data allows for the possibility of identification of faults that merit maintenance and repair in HVAC systems. In this paper we investigate possibilities for data-driven HVAC commissioning based on highly granular residential energy use data for a home in Austin, TX. The study aims is to identify possible HVAC deficiencies, and provide a preliminary methodology to identify one of these faults through energy use data analysis.