This is a guest post by Majid al-Dosari, a master’s student in Computational Science at George Mason University. I recently attended the first DC Energy and Data Summit organized by Potential Energy DC and co-hosted by the American Association for the Advancement of Science’s Fellowship Big Data Affinity Group. I was excited to be at a conference where two important issues of modern society meet: energy and (big) data!
— AAAS Big Data (@AAASBigData) June 27, 2014
There was a keynote and plenary panel. In addition, there were three breakout sessions where participants brainstormed improvements to building energy efficiency, the grid, and transportation. Many of the issues raised at the conference could be either big data or energy issues (separately). However, I’m only going to highlight points raised that deal with both energy and data.
In the keynote, Joel Gurin (NYU Governance Lab, Director of OpenData500) emphasized the benefits of open government data (which can include unexpected use cases). In the energy field, this includes data about electric power consumption, solar irradiance, and public transport. He mentioned that the private sector also has a role in publishing and adding value to existing data.
Then, in the plenary panel, Lucy Nowel (Department of Energy) brought up the costs associated with the management, transport, and analysis of big data. These costs can be measured in terms of time and energy. You can ask this question: At what point does it “cost” less to transport some amount of data physically (via a SneakerNet) than it does through some computer network?
After the panel, I attended the breakout session dealing with energy efficiency of homes and businesses. The former is the domain of Opower represented by Asher Burns-Burg, while the latter is the domain of Aquicore represented by Logan Soya. It is of interest to compare the general strategy of both companies here. Opower uses psychological methods to encourage households to reduce consumption. On the other hand, Aquicore uses business metrics to show how building managers can save money. But both are data-enabled.
Asher claims that Opower is just scratching the surface with what is possible with the use of data. He also talked about how personalization can be used to deliver more effective messages to consumers. Meanwhile, Aquicore has challenges associated with working with existing (old) metering technology in order to obtain more fine-grained data on building energy use.
In the concluding remarks, I became aware of discussions at the other breakout sessions. The most notable to me was a concern raised by the transportation session: The rebound effect can offset any gain in efficiency by an increase in consumption. Also, the grid breakout session suggested that there should be a centralized “data mart” and a way to be able to easily navigate the regulations of the energy industry.
While DC is not Houston, the unique environment of policy, entrepreneurship, and analytical talent give DC the potential to innovate in this area. Credit goes to Potential Energy DC for creating a supportive environment.