As power grids evolve to incorporate new variable renewable energy sources and adapt to growing peak electricity demands, balancing the grid—scheduling appropriate electricity supply to match demand—will become increasingly challenging.

The consumer's ability to shift electricity consumption to times when demand is lower or track consumption with the availability of renewable energy sources, known as demand response, is one approach to help maintain grid reliability and reduce electricity costs. Commercial buildings, which account for 37% of all electrical load in the United States, offer an opportunity to implement impactful demand response strategies.

"Commercial buildings provide an enormous grid balancing resource, but this resource has only been partially tapped. There is significant potential to increase commercial building demand response programs and load-side participation in electricity markets to support renewables integration, reliability and resilience," explained Johanna Mathieu, an associate professor of electrical engineering and computer science at the University of Michigan.

Mathieu and a team of researchers have presented a new open dataset in an accepted manuscript appearing in the Journal of Dynamic Systems, Measurement, and Control. The dataset will help researchers and practitioners better understand the role commercial buildings can play in balancing the grid.

Heating, ventilating and air conditioning (HVAC) systems are an ideal flexible load to target for demand response improvement as they account for almost half of commercial buildings' power consumption. Further, commercial buildings' high thermal inertia allows HVAC system power consumption to be altered for a short period of time before building temperatures noticeably change.

Proper grid balancing is best achieved with closed-loop control, incorporating feedback from the system, to achieve accurate power tracking. Current building automation systems—used to control and monitor variables like HVAC systems, lighting, plumbing, elevators, etc.—do not typically collect power measurements from HVAC system components. Whole-building electric meters usually measure power every 15 minutes, which is too infrequent for grid balancing, and do not provide a clear picture of HVAC system dynamics and responses.

To improve the availability of real-world demand response data, the research team developed the Sub-metered HVAC Implemented for Demand Response (SHIFDR) dataset. This massive dataset includes HVAC system component power measurements collected over five years from 14 University of Michigan campus buildings during typical operation and demand response events.

The dataset includes:

• Sub-metered fan power data at a one minute resolution
• Associated whole-building electric load data at a five minute resolution
• Building automation system data at a five minute resolution