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Resources and guidance for supporting employees, customers, and partners during this unprecedented health crisis.

  • September 2, 2020

The Sweatpants Effect

This is a syndicated post, view the original post here

Like many Americans, I’m working from home along-side my working spouse, kids and pet fish. Truthfully, I never believed last year’s entreatments: “We promise we will take care of the fish—you will never need to clean the tank.”  

My weekly patterns have shifted due to the closure of my office and local shelter-in-place orders. My commute is much better, which is great in the morning, but less great in the evening since I discovered an evening mental break is helpful.

When the stay at home orders were issued, there was some curiosity within Oracle Utilities around how the expected energy shift would impact individuals and utilities. There are some assumptions we can safely make: office energy usage declines and home energy usage increases.

One of my favorite changes is what we’ve termed “The Sweatpants Effect.” Our deep learning detection model on weekend dryer usage shows there is basically no change before and after shelter-in-place orders (Figure 1a). Laundry peaks around 9 a.m. and slowly drops over the course of the day.

Looking at weekday dryer usage (Figure 1b) we see that before shelter-in-place orders the dryer usage slowly rose throughout the day and peaked around 8 p.m. when people got back from work. But during the shelter-in-place order the weekdays now look very similar to the weekend. My conclusion is that sitting at home in sweatpants doing laundry during the day is no longer a weekend activity. And if it seems like the weekdays and weekends are blurring together, you’re right and this is supported by our deep learning analytics of usage patterns.




Figure 1 Impact of Covid on weekend (top) and weekday (bottom) dryer usage
.

The sharp-eyed reader will also note that weekday usage in figure 1a also appears to have shifted slightly to later in the morning.  This is clearer when we use our models to disaggregate water heating. The morning peaks in water heating usage has shifted by at least a full hour. The optimist in me hopes we are all working out, going for walks, and using our newly found commute time for personal and public good.


Figure 2 Water heating usage showing an hour shift in the morning peak.

And speaking of commutes —our models also show that electric vehicle (EV) charging has dropped substantially. This aligns with liquid fuel sales and is not very surprising.  


Figure 3 EV Usage dropping due to shelter in place orders.

Since Oracle Utilities offers personalized home energy management communication to millions of utilities customers using machine learning models, the data science team was also asked, “Do the models still work with such a large shift in energy usage?”  

In fact, the models weren’t specifically trained to distinguish between a work day and a weekend day when most people were home, and therefore the team was confident that the models would disaggregate stay-at-home usage without any issue. I’m happy to report that this is indeed the case; our deep learning models remain incredibly accurate. An example of this accuracy is shown in graph on a single-family home.


Figure 4 Comparison of the actual to predicted usage for a single family home.

Right now, my youngest is making a pitch for a pet hamster and assures me that I’ll never have to clean out the cage. I don’t need a machine learning model to know how that’s going to go.

Alex Panchula, PhD., is a director of product management for Oracle Utilities.

Oracle Utilities, including Opower, partners with the world's hardest working electric, water and natural gas companies to empower, enhance and enable your every single day. From cloud-native products and better grid management tools to support for every single step of your customer's journey, we have the answer. Learn more at oracle.com/utilities. Get specific product information as quick as clicking right here.

 

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