The Energy Efficient Path

The Energy Efficient Path
businessman drawing a line from point A to point B (selective focus)

Note: This article originally appeared on Energy Manager Today on August 18, 2014.

Getting from point A to point B sounds like a simple task.  Well, it used to be. If we use the analogy of a vehicle, we would historically have planned a journey by thinking about if we wanted to get there the quickest way possible or if the purpose was to take a more scenic route.  Or maybe we would have needed to stop at several locations on they way?

To expand this analogy and bring it into the 21st century, I might also consider additional factors like perhaps which vehicle is the most fuel efficient for the journey.  Or, if I had a hybrid-electric vehicle, I might plan my route to accommodate charging along the way.  Perhaps flying is best?  Should I offset the carbon I generate with a carbon offset purchase?

Operating a building management system (BMS) is like planning a trip.  A building management system is not unlike the example above where its’ operators are trying to get from one set point (point A) to a new set point (point B).  Now the question is, how to get there? Theoretically, this sounds like a simple task.  You just set your target (set point B) at the desired time. However, in a world with ever-fluctuating and increasing energy prices, more unpredictable weather, and more complex and integrated controls, getting from point A to point B for the lowest cost is complicated. It gets even more complicated when point B is a dynamic, moving target in an event-driven facility like an arena, conference centre or stadium.

The Energy Path Inside a Building

There are many paths in a building for delivering heating, cooling, ventilation, lighting, and water. Some paths are more cost effective than others. Turning down one unit is not effective if another unit compensates and the other unit is not as efficient at meeting the needs of the space.

Typically, a BMS is programmed with cooling and heating season control strategies.  However, these are programs with a static set of parameters.  They are static in the sense that operators often do no find the time to be able to manage set points according to the specific daily or hourly conditions.  As an example, cooling a space typically starts at a standard time before occupancy.  However, if software could help determine when outside air could be used rather than mechanical chilling, there is an energy savings opportunity.  What if the weather forecast changes suddenly and the knowledge of continuing cloud cover allows the BMS to extend the duration of outside air use to cool the spaces?  Adding to the potential for energy cost savings might be a utility rate structure that is advantageous to reduce energy consumption for the upcoming hour. Or perhaps the specific day is going to be extremely hot and humid?  In this case, it may be more advantageous to cool the space in the early morning hours when it is cooler outside and the electric utility rate is low.

This example describes a single air handling unit managed by a BMS.  In practical applications, there are hundreds or even thousands of devices which equate to thousands or hundreds of thousands of data points that combine to control the BMS and satisfy the occupant comfort-based conditions in a given space.  Energy efficiency initiatives must not sacrifice quality – this is paramount.  It’s a simple task to turn off the comfort cooling to save energy, but that is not a practical solution for the occupants.  It’s like driving your car at it’s most efficient speed (say 80 km/h) on cruise control without yielding to any stop lights.  On the highway, it’s too slow and in the city it’s too fast and dangerous. The trick is in making the small adjustments needed to adapt to any position.

Welcome to the 21st century where big data analytics, the Internet of Things, and cloud computing combine to help plan the most energy efficient path within the operator configured controls.