How the Super Bowl saves energy

Ryan Klein

Using demand data to understand human behavior during the big game

It’s only been a few days since the Super Bowl, when one of the world’s largest scale behavior modifications kicked off. In addition to the mass amounts of chips and dip consumed, there also tends to be a dip in energy consumption.

Major social events – and the Super Bowl certainly counts as that – have a modifying effect on human behavior. They cause predictable outcomes as we act as a more cohesive unit. In researching demand trends as part of a study of local reserve margins for the area around the stadium, we came across some interesting charts that show just how true this is – and how energy demand tracks what is going on in the big game and its immediate aftermath. Let’s take a look.

Super Bowl 50

Super Bowl 50 took place at Levi’s Stadium in Santa Clara, California. Santa Clara lies near the center of the PGE-TAC load region in the California ISO.

PGE-Map

 

It’s February 7, 2016, when the Denver Broncos beat the Carolina Panthers 24 to 10. The orange line is Super Bowl Sunday. All Sundays in 2016 leading up to the Super Bowl are indicated by the gray lines.

Here was the hourly demand curve for PGE-TAC zone, which includes Santa Clara:

Sunday-Load-PGE-TAC-with-Labels-1024x577

In this chart, there is a unique demand curve associated with the game. The day starts like every other Sunday – rising in the morning hours, declining in the afternoon and rising again in the evening. But during game time on Super Bowl Sunday, rather than seeing the usual convex shape during the evening hours we see a concave bend in as demand declines. Demand returns normal values after the game is complete. Clearly, the majority of viewers aren’t running their dishwashers or clothes dryers during the game. Also, more people are in one place at a Super Bowl party which means fewer individual appliances and TVs are running. And being the host city, many fans are enjoying the festivities near the stadium, or at their local watering holes.

The game started at 6:30 pm EST, which is 3:30 pm in California. This is right about the same time we start seeing a significant rise in demand that corresponds to the normal Sunday evening rise. However, we see demand dip below Sunday trends starting at 10:00 am PST. Perhaps many Californians decided to spend the day near the stadium enjoying the Super Bowl festivities, and thus weren’t at their homes to keep lights and appliances running. We see demand peak for the evening right at 7:00 pm PST, which would be right about the start of the third quarter of the game. During halftime, many fans take advantage of the break in gameplay to use the restroom, or refill on beverages and snack food, but once the third quarter starts demand experiences an immediate decline.

What is most interesting about this last chart is the increase in usage immediately following the game. As the game ended at around 8:15 pm PST, we see a leveling off of demand as game and party attendees make their way back to their homes and hotel rooms. As they turn on their lights and check their computers, we see demand flatten out, bucking the downward trend that’s typical on Sunday’s after 7:00 pm PST. By 9:00 pm PST demand has rejoined the typical Sunday curve and declines for the rest of the evening.

But what does the demand curve look like for Colorado and Carolina? Unfortunately, we will have to wait a bit to find out. Neither Denver nor Charlotte is in an ISO, hence other demand data is not as quickly available and will have to be analyzed. Still, it will be interesting to monitor demand in ISOs as they exist. After all, the Super Bowl brings the entire country together and molds our collective behavior over a short period of time like few other events. Do you want to dig into the demand data more? We’ve got a number of specialized datasets for demand analysis including the ISO views in the Velocity Suite – so sign in and check them out!

The Velocity Suite can be used to analyze more than just information related to load. For more information or questions about how to use these tools and analysts in Velocity Suite, feel free to Contact Velocity Support.

All graphics created using Velocity Suite.

Feature image: Arnie Papp using CC license via Flick

 

About the author

Ryan Klein

I’m a Market-Operations Analyst for the Velocity Suite providing Market Intelligence Services as part of Energy Portfolio Management in the Enterprise Software department of the Power Grids division within ABB. As a data expert, my responsibilities include ensuring the validity of data regarding North American energy generation, transmission, consumption, and demand. I’m also responsible for acquiring new data as it becomes available and relevant in energy markets and automating its inclusion to the Velocity Suite database.
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