Why my oil company can’t deliver

Well, my oil company can deliver and does so regularly – too regularly.  The problem is, if I didn’t have a whopping reserve fuel supply, I’d probably run out before they figured out it was time.  It’s not to say that the oil company doesn’t try to figure out my fuel use, but they’re stuck in a way of thinking that doesn’t accurately predict what I’ll consume.

In the oil industry, the determination to deliver oil is based on your home’s K-Factor.  The k-factor is a simple calculation for any given period – the number of elapsed degree days divided by the number of gallons of oil used in the period.  What’s a degree day, you ask?  A degree day is a measurement of how cold it is outside.  To figure out if your house needs heating, you take 65 degrees and subtract the average temperature for that day.  To calculate the average temperature, simply take the high temp plus the low temp and divide by two.  So, the formula for calculating one day’s degree day value is (65 – (( high + low ) / 2 ).  And thus, if 1000 degree days have elapsed and I’ve used 100 gallons of oil during that time, I’d have  K-Factor of 10.

The oil company, having measured degree days and oil use (because they know how much oil they delivered you) can now ostensibly predict when you’ll need more oil just by counting up the elapsed degree days.  For them, degree days / your k-factor = gallons of oil used.  The number gets low enough, they send out a truck for a fill-up.

I was curious as to how well this worked in reality, and I like collecting data about my energy usage.  I built a very simple linear regression with my predictor being degree days and my gallons of oil usage being my response.  Guess what, the prediction capability of using just degree days has an r-sqr of something like 10% for my house.  It’s an AWFUL prediction of my oil use.

And it’s easy to imagine why: how does it feel when it’s sunny out?  cloudy?  calm or windy?  raining or foggy?  I can think of lots of things that would appear to influence how much oil I’d use to heat my house.  Snow is a good insulator (at least the Eskimos seem to think so), I’d expect that would make my house more efficient to have a roof covered in snow.

Getting the extra data isn’t hard – NOAA offers up tons of weather data for free, so I went and got some more data.  I got data about precipitation, wind speed (both sustained and gust) and visibility (in my mind, a good measure of how cloudy it is) for every day since I’ve been collecting usage data on my oil consumption. 

I ran another regression analysis, and what’d I find out?  Precipitation?  Doesn’t seem to matter.  Visibility?  Doesn’t seem to matter.  Wind?  It matters.  I can up my prediction of my oil usage by adding the total sustained wind speed to the equation.  In fact, it increases from an r-sqr of 10% to about 83%.  Yup, you read it right.  But the oil company doesn’t use wind speed data to calculate what I might use in the way of oil – just degree days.

What would it do to the company’s delivery planning if they didn’t have to haul around a full truck of oil because it hadn’t been very windy and we were all using less oil?  They could make fewer, or at least better timed visits to the customer.  What if they could get me to a 1/4 tank of oil before filling me back up instead of a 1/2 tank?  You heard me right, they fill me up when my tank gets to about half because who knows why.  I’m guessing because they can’t predict my oil use that well and if they targeted a refill at a 1/4 tank, I’d run out of oil sometimes.

I get about 7 to 8 visits from the oil company to fill me up each winter.  Each fill up is about half a tank of oil.  So, if they could fill me when I’m 3/4’s empty, they could save half a visit each time.  That means every 3 visits would be reduced to 2 visits.  Instead of 7 or 8 visits, I might have 5 or 6.  At $4.60+ a gallon for diesel to fill up the truck, saving 2 visits to every customer they have every year would be worth quite a bit.  And what’s horrible about it is a little extra free information is all they’d need to cut visits and still not have anyone run out of oil.

9/3/08 EDIT:  In the spirit of full disclosure, I did some more research on my oil usage because I paid for some upgrades to my system to improve efficiency.  I found that degree days makes a much better predictor than my prior experiment indicated.  This is especially true if I remove data about the warmer months – usually from late April/May when they fill me up at the end of the season through late Aug/early Sept when they fill me up for the start of the heating season. 

In fact, I just got a delivery today which was for an unusually large amount of oil probably because they can’t predict summer use.  I still see problems in the residuals suggesting that another variable is needed.  Regardless, my argument holds, if the oil company had a better model for predicting my use, they could fill me up less often and save lots of cash.  I should note, I have a very, very old house, so wind might have a bigger effect on my poorly insulated home than it does for most people.