HowPower

Energy efficiency is expected to be the most used and least cost option of meeting increasingly aggressive energy and greenhouse gas (GHG) reduction targets, resulting in U.S. utility funded energy efficiency programs growing from $4.8b in 2010 to a projected $11 billion in 2025. Behavior specific energy efficiency programs are estimated to have the potential to achieve 20% residential energy savings, but the state-of-the-art program achieves only 1.5% savings - in other words, there are currently no deep energy-saving, scalable, cost-effective residential or small and medium business (SMB) energy efficiency programs. Our solution is a virtual ‘concierge’ that ushers people around their home to make their own upgrades without requiring a human expert. It is a browser application - usable on any device, wherever energy needs saving, with no download needed. In order to address weaknesses in other programs and achieve large population-wide energy savings, we focus on: (a) Deep energy savings per user, (b) Large-scale reach, and (c) Advanced evaluation, measurement, and verification (EM&V) to provide stronger causality in program evaluation, where proof of savings is key to unlocking utility incentives. The browser application was developed by leaders in energy behavior change, is based on empirical learnings across multiple fields and a large related initiative at Stanford, and was developed with iterative R&D on thousands of users. The key next step is to perform a pilot, which we intend to do this summer and fall, in order to demonstrate the efficacy of the product to potential utility customers and partners - several are interested. If effective, most likely revenue arrangements with such entities would include licensing or lead generation fees, to Stanford or a Stanford related non-profit. The ultimate goal is to scale and achieve energy savings throughout the United States and potentially beyond. Deliverables from this funding will include a completed early stage commercial grade version of the browser application for the pilot (it is currently close to complete); code to support data acquisition, storage, and analysis; and the associated data/results.

Team Members

Carrie Armel (Research Associate, PEEC), Prof. James Sweeney (MS&E)