The advent of massive data sets and advanced analytical techniques is changing information technology in fundamental ways. In the past, data was used primarily to confirm hypotheses. Increasingly, data is used to generate hypotheses, indicate trends, identify anomalies, and make predictions. This new paradigm is impacting research in diverse fields and business in diverse industries. The impact is particularly great in energy and environment. This is driven by the proliferation of sensors measuring physical properties, the availability of vast networks of human data, the algorithms to extract information, and the computing power to do this in real time. This is an area where data, tools, and applications are closely linked.
Big Data for Energy and Environment
The Spring 2014 conference of Stanford Energy 3.0 on Big Data for Energy and Environment was held on May 15, 2014. The conference included speakers from Stanford and industry, who focused on the impact of massive data and sophisticated analytical techniques in fields such as electricity generation and distribution, manufacturing, oil and gas, and sustainability. Topics included:
General data science techniques and sensors
Margot Gerritsen, Data science and analytics
Phil Levis, Embedded sensing systems
Oil and gas exploration and production
Biondo Biondi, Permanent seismic arrays
Jef Caers, Multiple-point geostatistics
Environment and climate change
Noah Diffenbaugh, Climate risk
Mary Ruckelshaus, Earth Genome Project
Annie Hazlehurst and Sara Menker, Gro Intelligence
Electric grid, energy efficiency, and social networks
Balaji Prabhakar, Societal networks
Ram Rajagopal, Electric grid analytics
Amit Narayan, AutoGrid: Finding business value from energy analytics
For more information, please go to the conference website.
