← Blog Home

The Effect of Weather Normalization on Energy Consumption

Summer is home improvement season at our house. After seeing how much energy Tendril’s web portal users are saving switching to LED bulbs, one of our projects this year is to install a bunch of LEDs. Those bulbs aren’t cheap, so naturally I want to understand how much I’m saving so I can feel like I’m getting my money’s worth. The question is:

Am I using less electricity with LEDs?

This seems like a really simple ask – I can just look at my bills, right? According to my bill, I spent $51.00 in June this year. Compare that to last June (pre-LEDs) when I spent $70.91. Looks like I saved $20.91!

But wait – there were some different fees and charges on my bill this year. And the price of electricity was different. Easy enough, I’ll just look at my usage in kWh: 414 kWh this June compared to 600 kWh last June. Savings, again! 

But wait – the billing periods were different lengths. Ugh, okay, I can do a little math. That equals 15.3 kWh per day this June and 21.4 kWh per day last June. Hooray! 

But wait – the average temperature was 67°F this year compared to 72°F last year. Of course I used less energy. Five degrees makes a huge difference in how much air conditioning we use, and last year was hot. So were we more energy efficient this year, or did we just get lucky with mild summer weather?

Let’s face it: most people would have stopped at step 1 of the analysis. Some savvy energy customers may have attempted steps 2 and 3. Though it’s pretty unlikely that your typical energy user will know how to handle step 4 because it involves some complex math. But there’s no way to accurately answer the question without adjusting for the effect of weather.

The team at Tendril has spent a lot of time grappling with this problem. Specifically, how can we answer this complex question in a simple way?

There are a number of different ways to address this challenge. For example, you could simply plot average temperature on top of a billing history graph. That helps customers see patterns, but it doesn’t give any definitive answers. 

But to really get it right, and ensure customers get accurate data personalized to their home and location, we must turn to weather normalization modeling. This means building a statistical model that correlates energy consumption to temperature (via heating degree days and cooling degree days). Statisticians in the house will appreciate this method, but your average energy customer doesn’t have any reason to care about models, coefficients, or predictions.

To simplify all of this, we’ve developed a new feature that leverages the math but focuses on the core question: Am I using less electricity? 

Our results section skips the direct bill comparison and jumps right to the numbers that matter. For example, a customer’s results might say: You were six percent more efficient this year, which means you saved $11 on your bill. (I’ll get back to my example in a minute).

For those who are curious, here’s how weather normalization works. To answer the question 

“Am I using less energy this year compared to last year?”

We ask a slightly different question:

“How much energy would I have used last year if the weather was the same as this year?”

Think of last year as the benchmark. Based on billing history and the temperature last year, we can build a statistical model to predict energy use – unique for every customer for any given temperature. Then, we plug in the actual temperature from this year to get an energy use prediction. By comparing the prediction to the actual kWh usage per day, we can determine savings (or spending). 

Which brings me back to my story: my result for June is that I actually used two percent more energy than last year. How? When you adjust for weather, it turns out I didn’t use less energy as my bills suggested – I just benefitted from mild weather. Average outdoor temperature has a huge impact on energy bills (think heating and cooling), which is why weather normalization is so important. 

What weather normalization doesn’t tell me is why I used more energy. (Fortunately Tendril has another model for that – yay, physics!) But it’s enough information to motivate me to take action. Just think of how much we would have used if we hadn’t installed those LEDs. We must be using a lot more energy somewhere else. Come to think of it, I’ve been turning down the thermostat a lot more lately. Time to adjust my thermostat program as we get into the hottest months of the summer…




  • Continuous Demand Management
  • Customer Ops & CSAT
  • DERs
  • DSM
  • Data Analytics
  • Demand Response
  • Disruption
  • Energy Efficiency
  • HERs
  • High Bill Alerts
  • Privacy & Security
  • Smart Home
  • Solar

    Invalid Email Address

    Subscribe to This Feed

    Thank you for subscribing to Behind the Meter Blog.