the savings lie. There are tons of data sources out there to map together to help with this:
* Take a look at the Energy Information Administration’s (EIA) website. If you’re a data hacker and interested in cleantech, it’s a feast for sore eyes. Tons of data. What if we mashed the EIA data together with real estate data or GIS data, especially with thermographic mapping?
* Another area is making basic savings predictions based on energy intensity—the measure of energy use per square foot of a home. With basic bill data, square footage and age of home data from the MLS, you could probably get something really interesting going.
* Still another area is statistical analysis. Utilities have huge energy efficiency targets to reach (see here). One of the key challenges is figuring out what is cost effective and what’s not. For example, if they subsidize a CFL bulb, they need to measure kilowatt-hours saved, how much is significant to the program vs. what would have happened anyway via regular market adoption, what’s the actual savings per dollar, etc. This gets complicated quickly and goes bezerko when carbon legislation kicks in.
Building Analysis and Interoperability
One of the hassles of the industry is predicting the energy use of a home based on an in-home energy audit. There are a ton of tools for this of varying accuracy and complexity. If you’re interested at all in this space you need to know about the HOME STAR Bill. HOME STAR will offer a $3,000 tax credit if a home is made 20 percent more efficient. So, the question is, regarding 20 percent, who says and with what tool?
A few challenges and opportunities:
* Obviously, there can be algorithmic improvements here—you’ll need to know building science, which fortunately isn’t rocket science. The main introductory textbook for the field is Residential Energy.
* There are big workflow and modeling issues with home energy audits. Recurve is a software company working on this, and I’m sure they are hiring.
* Interoperability is a huge issue here. There are literally hundreds or thousands of different energy programs that industry participants need to work across. Of note is financing, which is growing quickly in this space in both secured form (see PaceNow.org) and unsecured. The financing systems (themselves nascent and a software opportunity) and the audit sources need to communicate, so we need interop. Some folks have begun working on this, but there’s a long way to go.
Wrapping Up and a Shameless plug
Hopefully this gives you a sense of some of the cool challenges at the intersection of energy efficiency and software. And this is just a small sampling of some of the opportunities.
Our own company, EnergySavvy, is ramping up quickly, and we’re hiring. I didn’t delve much into what we do, but suffice it to say, we have big technical challenges ahead. Currently, we use Python, Django, Postgres, PostGIS to weave together energy modeling algorithms, energy data, rebate info, utility and municipality service area mapping and workflow. And that’s just the beginning—we have big plans ahead. Think of what you see at EnergySavvy.com as merely a prototype from a technical perspective.
We also started a LinkedIn group for developers working in energy efficiency, or developers who want to make the transition into cleantech but aren’t sure where to start.