Harnessing the Power of Data: Sustainable Energy Transitions Initiative Conference
| dc.contributor.author | Girardeau, Hannah | |
| dc.contributor.author | Phillips, Jonathan | |
| dc.contributor.author | Jeuland, Marc | |
| dc.date.accessioned | 2024-11-26T19:24:36Z | |
| dc.date.available | 2024-11-26T19:24:36Z | |
| dc.date.issued | 2018-06-28 | |
| dc.description.abstract | On May 15–17, 2018, more than 100 academic researchers and energy access practitioners gathered at Duke University to discuss critical issues related to energy access as part of the third annual conference for the Sustainable Energy Transitions Initiative (SETI). Presentations by Kyle Bradbury of Duke University, Johannes Urpelainen of Johns Hopkins University, Nathan Williams of Carnegie Mellon University, and Jay Taneja of the University of Massachusetts–Amherst highlighted remarkable advances in energy data analytics, described applications for developing world energy challenges, and outlined remaining data-related hurdles impeding progress on energy access. Energy developers, utilities, planners, and policy makers are often not equipped with the necessary tools to understand the changing landscape of energy delivery options and customer preferences. Researchers and grid operators are often restricted by outdated, unavailable, or biased data in the field. Through innovative methods and analytical tools, such as remote sensing, satellite imagery, and machine learning, data analytics are improving our understanding of energy demand in rural areas, customer needs and expectations, the local availability of energy resources, and the realities of providing electricity to underserved communities. These proceedings present key conference takeaways related to the core theme of energy data analytics. | |
| dc.identifier.uri | ||
| dc.publisher | Nicholas Institute for Environmental Policy Solutions | |
| dc.rights.uri | ||
| dc.subject | energy access | |
| dc.subject | energy data analytics | |
| dc.subject | remote sensing | |
| dc.subject | satellite imagery | |
| dc.subject | machine learning | |
| dc.title | Harnessing the Power of Data: Sustainable Energy Transitions Initiative Conference | |
| dc.type | Report | |
| pubs.organisational-group | Duke | |
| pubs.organisational-group | Nicholas School of the Environment | |
| pubs.organisational-group | Sanford School of Public Policy | |
| pubs.organisational-group | Duke Population Research Institute | |
| pubs.organisational-group | Environmental Sciences and Policy | |
| pubs.organisational-group | University Institutes and Centers | |
| pubs.organisational-group | Duke Global Health Institute | |
| pubs.organisational-group | Duke Population Research Center | |
| pubs.publication-status | Published online |
Files
Original bundle
- Name:
- harnessing_the_power_of_data.pdf
- Size:
- 1.16 MB
- Format:
- Adobe Portable Document Format
- Description:
- Published version