FM Articles

A Proven Approach to Optimizing HVAC Systems


Heating, ventilation, and air conditioning (HVAC) – the chilled water plant, steam and hot water plant, and air distribution system – typically consumes 33 percent of the electricity and 56 percent of the natural gas in healthcare facilities, and 42 percent of the electricity and 77 percent of the natural gas in college and university educational buildings. Chilled water systems also consume substantial amounts of water. That makes these systems a ripe target for optimization: minimizing their energy and water use can make a major contribution to reaching sustainability goals and reducing operational costs.

HVAC efficiency projects, however, often fail to deliver on their promise. In environments such as labs and operating rooms, where maintaining precise temperatures is essential and maintaining patient comfort is an overriding need, even new, state-of-the-art HVAC systems may lose efficiency after installation. This happens when system operators, faced with pressing operational needs, understandably take control, overriding set points and sacrificing efficiency. Fortunately, decline is not inevitable. There’s a proven approach to HVAC optimization projects that reliably delivers maximum results with little or no performance drift.

Optimization Step by Step
The goal of optimization is to make mechanical systems work at peak effectiveness all the time. Healthcare and campus facility directors can optimize even the most demanding environments, outfitted with new or existing equipment, with a combination of energy engineering, relational control software, and a technical support platform that keeps systems at commissioned levels.

The most successful optimization projects follow these three guidelines:

  1. What cannot be measured cannot be optimized. Without an accurate measure of energy use by each piece of equipment in the system, it is impossible to accurately predict and report the impact of varying conditions on the system.
  2. Optimize systems, not just individual components. If an optimization plan focuses only on installing the most efficient pieces of equipment without considering how to maximize performance of the whole system, it won’t capture the total available system efficiency. Holistic automated optimization of HVAC systems typically increases energy efficiency by an additional 10 to 25 percent over just installing new equipment.
  3. Optimization must be automatic, dynamic, and continuous for maximum efficiency. Optimization should be a real-time dynamic process, not a static set-and-forget process. If a plant’s operational control is not based on real-time inputs, it cannot be fully optimized.

The basic optimization process starts with calculating the current performance of the HVAC system to determine the potential energy savings. A detailed engineering analysis can show an hour-by-hour simulation of the system’s baseline performance against weather data and load profiles for a full year. Next, a scope-of-work document should identify the electrical, mechanical, and control upgrades needed for a holistic optimization program. With that in hand, the engineering team can create a second model that simulates the system’s post-upgrade performance and determines the project’s energy and cost savings.

Finally, a life-cycle cost analysis should take into account the full implementation costs (mechanical, electrical, controls, optimization, project management, qualification, sales taxes, affiliate staff fees, information technology costs, permits, commissioning, engineering, contingency, and so on), the cost of money, depreciation, utility incentives, and maintenance to calculate the project’s internal rate of return and net present value. That data supports the business case for the project.


Typical optimization projects involve a combination of the following energy conservation measures:

  • Match the system to actual use requirements, ensuring proper temperature and relative humidity set points, air-change-per-hour requirements, and space pressurization.
  • Install instrumentation (such as flow meters, power meters, temperature, and relative humidity sensors) to enable real-time control decisions, reporting on key performance indicators, and measurement and verification.
  • Install variable frequency drives on air-handling unit fans, chillers, boiler combustion fans, chilled and hot water pumps, condenser water pumps, and cooling tower fans.
  • Install modern AHU, chiller, and boiler controls.
  • Eliminate bottlenecks (such as hot or rogue zones and undersized terminal units) and mixing by replacing three-way temperature control valves with two-way control valves and closing decouplers.

Once these measures are implemented, real-time optimization software can operate the system for both cost efficiency and energy efficiency. For example, intelligently resetting the supply air temperature on an AHU will reduce simultaneous heating and cooling while allowing the system to reset chilled water and hot water temperatures back at the utility plant. An automated system can also control condenser water pumps, cooling towers, and chillers based on their relationship to one another. The end result is an opportunity to reduce the facility’s energy bills by over 20 percent – we typically see 20 percent to 25 percent reductions with software alone.

Real-World Results Prove the Value
The University of Texas at Austin is saving 21,000,000 kWh, 200,000 MMBtu of steam usage, and 4 million gallons of water annually after a series of optimization projects that followed this process. This is an unusually expansive example – UT Austin is one of the largest public universities in the U.S., with a 350-acre main campus supporting 21,000 faculty and staff, 17 colleges and schools, and more than 50,000 students. Because of that, the optimization projects encompassed a variety of situations that other campuses also face, and proved out the process. The university has optimized four chilled water plants (totaling 45,000 tons) on a common loop with a 4 million gallon thermal energy storage (TES) tank, control of chiller staging for all four plants, and variable differential pressure and flow control.

At the Penn State Health Milton S. Hershey Medical Center, results exceeded expectations. After the center optimized 12 chillers – eight in the central plant and two in each of the two satellite plants – its energy intensity dropped 4 percent. The project is yielding 4.16 GWh in annual savings, versus the 3.4 GWh facility leaders anticipated when they embarked on the project.

Projects like these show that hospital and higher education facility directors can wring savings out of even the most demanding environments, with new or existing equipment, by following the laws of optimization.

Ian Dempster is senior director of product innovation at Optimum Energy and a certified energy manager (CEM). He directs multiple simultaneous R&D projects, drawing on a 16-year engineering career that spans three continents.