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PIXELS to KNOWLEDGE extracting insights from energy data through - PowerPoint PPT Presentation

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from PIXELS to KNOWLEDGE extracting insights from energy data through visualization kyle bradbury, phd ENERGY DATA ANALYTICS LAB milestones in energy through VISUALIZATION Early Power Plants Technical Drawings 1869 Source: Babcock

  1. from PIXELS to KNOWLEDGE extracting insights from energy data through visualization kyle bradbury, phd

  2. ENERGY DATA ANALYTICS LAB

  3. milestones in energy through VISUALIZATION

  4. Early Power Plants Technical Drawings 1869 Source: Babcock & Wilcox Company. Steam, its generation and use . Babcock & Wilcox., 1922.

  5. Thermal Energy Efficiency, 1898 Sankey Diagrams Source: https://en.wikipedia.org/wiki/Sankey_diagram#/media/File:JIE_Sankey_V5_Fig1.png

  6. U.S. Energy Use, 2014 Sankey Diagram

  7. Electrification of the U.S., 1921 Cartogram Source: http://www.firstgreen.co/2013/08/graph- of-the-day-map-of-u-s-electricity-consumption-in- 1921/

  8. Oil Trade 1969 flow diagram

  9. Hubbert Curve 1956 line plot Source: Hubbert’s Peak, from M. King Hubbert , “Nuclear Energy and the Fossil Fuels,” presented at a meeting of the American Petroleum Institute, 1956.

  10. Hubbert Curve 2000 line plot Actual Production Hubbert’s Predicton Source: https://en.wikipedia.org/wiki/Hubbert_peak_theory#/media/File:Hubbert_Upper-Bound_Peak_1956.png

  11. Hubbert Curve 2014 line plot Actual Production Hubbert’s Predicton Source: https://en.wikipedia.org/wiki/Hubbert_peak_theory#/media/File:Hubbert_Upper-Bound_Peak_1956.png

  12. Atmospheric CO 2 2006 line plot Source: NOAA. http://climate.nasa.gov/vital-signs/carbon-dioxide/

  13. Atmospheric CO 2 , 2006 line plot Left: https://filmefuerdieerde.org/en/films/climate/an-inconvenient-truth Right: http://www.moviesteve.com/wp-content/uploads/2013/09/inconvenient_truth1.jpg

  14. History of U.S. Energy Consumption, 2009 line plot Petroleum Natural Gas Coal Nuclear Hydroelectric Wood Source: U.S. Energy Information Administration – Annual Energy Review 2009

  15. U.S. ELECTRICITY generation

  16. Energy Information Administration (EIA) http://www.eia.gov/state/maps.cfm?src=home-f3

  17. Capacity LINK

  18. Generation

  19. CO 2 Emissions

  20. Generator Age

  21. U.S. Generation LINK

  22. energy resource assessment from REMOTE SENSING data Jordan Malof Kyle Bradbury Rui Hou Richard Newell Leslie Collins Energy Initiative SSPACISS Laboratory

  23. Oahu, Hawaii New Solar Arrays after 2008 Kyle Bradbury & Mengyang Lin LINK

  24. Oahu, Hawaii New Solar Arrays after 2008 Kyle Bradbury & Mengyang Lin

  25. Oahu, Hawaii New Solar Arrays after 2008 Kyle Bradbury & Mengyang Lin

  26. Oahu, Hawaii New Solar Arrays after 2008 Kyle Bradbury & Mengyang Lin

  27. the problem Interest exists in quantifying U.S. Theoretical illustration of solar distributed solar power capacity panel capacity by region more Capacity estimates are difficult to obtain less – Large-scale audits are conducted with questionnaires Malof , J. M., R. Hou, L. M. Collins, K. Bradbury, and R. Newell, “Automatic solar photovoltaic panel detection in satellite imagery,” in 2015 International Conference on Renewable Energy Research and Applications (ICRERA), 2015, pp. 1428 – 1431.

  28. machine learning solution Example e of of imag agery ery data ta High resolution satellite images are increasingly available Algorithms may automatically estimate power capacity from images Solar r arra ray Malof , J. M., R. Hou, L. M. Collins, K. Bradbury, and R. Newell, “Automatic solar photovoltaic panel detection in satellite imagery,” in 2015 International Conference on Renewable Energy Research and Applications (ICRERA), 2015, pp. 1428 – 1431.

  29. detection algorithm development Our development dataset of 100 images were extracted 100 house images from US Geological Survey 50 with solar panels (red) satellite imagery (right) … ML algorithms were developed 50 without solar panels to automatically locate the … solar panels Malof , J. M., R. Hou, L. M. Collins, K. Bradbury, and R. Newell, “Automatic solar photovoltaic panel detection in satellite imagery,” in 2015 International Conference on Renewable Energy Research and Applications (ICRERA), 2015, pp. 1428 – 1431.

  30. A snapshot of the algorithm Malof , J. M., R. Hou, L. M. Collins, K. Bradbury, and R. Newell, “Automatic solar photovoltaic panel detection in satellite imagery,” in 2015 International Conference on Renewable Energy Research and Applications (ICRERA), 2015, pp. 1428 – 1431.

  31. solar array detection algorithm performance 92% of panels were curve identified, with 4 total false alarms Malof , J. M., R. Hou, L. M. Collins, K. Bradbury, and R. Newell, “Automatic solar photovoltaic panel detection in satellite imagery,” in 2015 International Conference on Renewable Energy Research and Applications (ICRERA), 2015, pp. 1428 – 1431.

  32. visualizing results – houses with panels Black polygons are labeled solar arrays Yellow ellipses are detected regions Malof, J. M., R. Hou, L. M. Collins, K. Bradbury, and R. Newell, “Automatic solar photovoltaic panel detection in satellite imagery,” in 2015 International Conference on Renewable Energy Research and Applications (ICRERA), 2015, pp. 1428 – 1431.

  33. visualizing results – houses without panels Black polygons are labeled solar arrays + Yellow ellipses are detected regions Malof, J. M., R. Hou, L. M. Collins, K. Bradbury, and R. Newell, “Automatic solar photovoltaic panel detection in satellite imagery,” in 2015 International Conference on Renewable Energy Research and Applications (ICRERA), 2015, pp. 1428 – 1431.

  34. Data+ team created a ground truth data set of over 19,000 solar array locations

  35. ENERGY data analytics lab

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