From the Nvidia article:
“AI is extending into every facet of our lives: how we travel, how we produce food, how we work, how we live” …“Smart buildings are one of the most valuable and largest opportunities for this trend.”
One example is wind farm efficiency:
For example, GE’s PowerUp Platform has been extended to become the Digital Wind Farm. With this solution, GE extends analytics and optimization beyond a single wind turbine to the entire wind farm. GE harnessed the power of the emerging Industrial Internet to create the Digital Wind Farm, a dynamic, connected, and adaptable wind energy platform that pairs wind turbines in a wind farm with digital infrastructure to optimize efficiency across the entire wind farm. This platform can account for the wind farm’s topology, surrounding geography, wake effects, and other inputs to control individual wind turbines and optimize the operation as a whole. Through these techniques, the Digital Wind Farm technology boosts a wind farm’s energy production by up to 20 percent and could help generate up to an estimated $50 billion value for the wind industry. The Digital Wind Farm uses interconnected digital technology to address a long-standing need for greater flexibility in renewable power.
Overall, the report’s projections show significant potential, though much work is needed to translate potential into reality.
Brooklyn Microgrid is using the blockchain for smart contracts and currencies in support of “resilient, sustainable and more efficient energy production of the future.” In simple terms, as FastCompany explains:
On one side of President Street, five homes with solar panels generate electricity. On the other side, five homes buy power when the opposite homes don’t need it. In the middle is a blockchain network, managing and recording transactions with little human interaction.”
Accenture’s use of a digital platform for speed, scale, and efficiency.
Information systems for managing energy and carbon emissions data are critical to corporate environmental sustainability efforts.
Yet there is a lack of research on how these systems are adopted, used, implemented, etc.
To shed some light on this topic, I decided to explore how organizations are implementing and applying ECMS. Together with my colleague Ryan Whisnant, we looked at two different systems in two different organizations to identify patterns of use and make recommendations for practice.
Here’s the abstract and a link to the SSRN paper (a revised version of which is forthcoming at the Journal of Industrial Ecology):
This article examines an important class of information system (IS) that serves as the foundation for corporate energy and greenhouse gas accounting: energy and carbon management systems (ECMS). Investors, regulators, customers, and employees increasingly demand that organizations provide information about their organizational energy use and greenhouse gas emissions. However, there is little transparency about how organizations use ECMS to meet such demands. To shed light on ECMS implementation and application, we collected extensive qualitative interview data from two service-sector organizations: one that uses a spreadsheet-based ECMS and another that implemented an ECMS provided by a third-party vendor. Our analysis of collected data revealed numerous challenges in the areas of business processes, managerial capabilities, data capture and integration, and data quality. Though our results derive from only two organizations and require confirmation in large-sample surveys, we provide several general recommendations for organizations regarding ECMS. We also provide suggestions for future studies to build on our tentative results. Link: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2411879
While this paper represents an important early step in this research stream, much more needs to be done. In particular, one of the most important next steps would be to examine the business value implications of these systems.
According to the Edison Foundation, in the U.S. alone there are over 46 million smart meters installed as of July 2013.
At a read rate of 24 per day, this equals to about 1.1 billion data points per day, or just north of 400 billion per year.
What to do with all this data?
Greentechmedia reports that at the recent Soft Grid 2013 conference, several views were shared:
This is the beginning of a very long evolution that may or may not end in our lifetimes
– Josh Gerber, smart grid manager for San Diego Gas & Electric
You have to reach out to those customers and give them something that interests them
– Scott Young, senior director of software platforms for Silver Spring Networks
Other examples that I quote from the greentechmedia article include:
- analyze household power usage data to help customers learn where they’re wasting energy, how different appliances are affecting their usage, and the like — in other words, to turn utilities into “trusted energy advisors” to their customers.
- predict individual customers’ likelihood to want to sign up for load curtailment programs (demand response), or perhaps install solar panels on their property.
- pairing third-party information of the kind you might get from the retail and customer service bid data world with the usage details … from their meters
This is a new scale of data, and extracting valuable insights will require thoughtful pairing of user needs with technological capabilities to create compelling new services. In other words, turning data into information and knowledge for better decision making. It can’t happen fast enough.