“We’ve got to be able to prove the accuracy of our energy-savings predictions to be able to sell home performance.”
I’ll reject that premise outright. It’s just not necessary. I’ll go further to suggest that if you’re relying on energy-savings alone to drive you’re business, you’re going to struggle. Unless energy prices shoot up, savings won’t make the phone ring. And savings won’t make most sales.
That said, the better you can predict energy savings, the better off you are.
Now, let me try to reconcile this.
First, you don’t need accurate energy-savings numbers for the simple reason that the driving motivator for most home improvement purchases isn’t energy savings. There are a lot of other benefits, from comfort, to reducing ice damming problems, to fixing wet, stinky crawlspaces that people are willing to pay for. Making the daughter’s bedroom livable all winter or the home office usable in the summer are often more important than energy savings. (Corollary: if you rely on the cost-effective energy-savings argument alone, you will have much smaller projects. Energy prices are too low to drive deeper saving projects.)
I’ll also note that accurate (or precise) savings numbers aren’t necessary to establish very robust energy-savings guarantees. Perry Bigelow did this in Chicago back in the late 80s/early 90s on affordably-built new homes.
And note, I do not mean doing this by using any existing modeling tool which can give very precise and wildly inaccurate numbers. Bigelow knew he couldn’t precisely dial in the savings numbers, but he could still offer eye-popping guarantees simply by being very conservative in his assumptions. The key here is “conservative”. I don’t want to hit 95% of my prediction. Or 99%. And 60% won’t fly at all. Nor don’t I want to fall short of the target half the time. I want to overdeliver 100% of the time—or very close to 100% of the time.
So, on to the second part of the equation. The better you can predict energy savings, the higher you can ratchet up your predictions and the more you can differentiate yourself. The more experience you gain, and the more results you see, the better your internal prediction can before, and the higher your customer-facing guarantee can be.
There are other reasons, outside what is necessary to run your business today, why better savings predictions will be useful. Unlocking financial markets is one of them. But you don’t have to wait for that to be successful fixing people’s homes today. You do, though, need to stop hiding behind models. And stop hiding behind program requirements to use models. If you want the program cheese, follow the program requirements. But you can sell the benefits your customers want, explain what the program mumbo jumbo means (but only if you choose to participate in a program), and be very clear about what you’re going to deliver—and then deliver it–models be damned.