The project — which is being tested at various sites, including at Johnson Controls in Milwaukee and buildings on the Stanford University campus — are to serve as a foundation for advancing HVAC control systems in general. Rawlings, chemical and biological engineering Professor Christos Maravelias, and Ph.D. students Michael Risbeck and Nishith Patel are developing algorithms designed to enable building managers to harness a range of data to run their HVAC systems more efficiently.

Building managers haven’t always had much data to inform decisions about heating and cooling, said Rawlings, but in recent years, it’s become easier to acquire the kind of data that work well for control processes. These data include energy pricing, weather forecasts, worker hours, and the impacts of other potential heat sources in a building.

However, there is no standard way to put together an HVAC system for a large building, and even within a single building, that system might be composed of many disparate pieces of equipment and several different control systems. To analyze and control the system as a whole — or optimize HVAC across an entire campus of buildings — the control framework has to be agnostic to the size and makeup of the system. The University of Wisconsin-Madison researchers are using a control method called model predictive control, which involves forecasting the future behavior of a system and taking those forecasts into account to make real-time adjustments.

“In the chemical industry, optimization has been a much more important part in the operation of chemical plants, because that’s your profitability, that’s the goal of the business,” Rawlings said. “So what the HVAC industry is able to do now is take advantage of all that development over the last 20 or 30 years in the chemical industry and bring it over to buildings.”

As the optimization algorithms pass through different revisions and iterations, Rawlings and his group expect to gain greater understanding of the real-world context of their ideas. “You know how the building is running and you can look at how it ran a year ago, two years ago, 10 years ago, and you can detect things like equipment that is degrading, and you can find out what the use patterns are,” said Rawlings. “You can find out that over time, energy use in this building is rising or dropping, and you can start to ask what-if questions.”

Robert Turney, a Johnson Controls engineer and technical lead on the project, said the work is driven by customer interest in improving energy efficiency and cutting costs. He credits the University of Wisconsin-Madison researchers with helping the controls industry seize on new possibilities.

Publication date: 2/23/2015

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