Decoding the SAVE project data

Presented to SAVE project open day participants, University of Southampton, 11th May 2017

Transcript

1. Decoding the SAVE project data Tom Rushby t.w.rushby@soton.ac.uk @tom_rushby Ben Anderson b.anderson@soton.ac.uk @dataknut
2. Menu § Data collection – Loop data journey – What else are we collecting? – Constructing the dataset 2 § Exploring the data – Become an energy analyst § What next? Image (modified): ‘matrix’ by Gamaliel Espinoza Macedo https://www.flickr.com/photos/gamikun/
3. § Data collection – Loop data journey – What else are we collecting? – Constructing the dataset Menu 3 Image (modified): ‘matrix’ by Gamaliel Espinoza Macedo https://www.flickr.com/photos/gamikun/ § Exploring the data – Become an energy analyst § What next?
4. Data collection 4 § Loop data journey (1) Photos by Patrick James | p.a.james@soton.ac.uk | @pab_james
5. Data collection 5 § Loop data journey (2)
6. Data collection 6 § Loop data journey (3)
7. Data collection 7 Energy Loop Recruitment survey Time-use diaries Update survey SAVE ý þ þ þ § What are we collecting? Presenting today?
8. Constructing the dataset 8 Analysis and reports Survey updates Recruitment survey Energy Loop data
9. Constructing the dataset 9 § How much data? Ø Meter Clamps o (4,318 households) o 15 minute consumption (Wh) à 414,000 records/day o 10 second power (W) à 37m records/day Ø Collecting for ~3 years o ~2 Terabytes
10. Menu 10 § Data collection – Loop data journey – What else are we collecting? – Constructing the dataset § Exploring the data – Become an energy analyst § What next?
11. Exploring the data § We will use different time scales to reveal patterns in consumption Today we will look at data collected over: • Months • Weeks • Hours 11
12. Exploring the data 12 0.06 0.09 0.12 0.15 Thu May 19 Thu Jun 02 Thu Jun 16 Thu Jun 30 Thu Jul 14 Thu Jul 28 Thu Aug 11 Thu Aug 25 Thu Sep 08 Thu Sep 22 Thu Oct 06 Thu Oct 20 Thu Nov 03 Thu Nov 17 Thu Dec 01 Thu Dec 15 Thu Dec 29 Thu Jan 12 Thu Jan 26 Thu Feb 09 Thu Feb 23 Thu Mar 09 kWh Let’s look at this period 0.06 0.09 0.12 0.15 Thu May 19 Thu Jun 02 Thu Jun 16 Thu Jun 30 Thu Jul 14 Thu Jul 28 Thu Aug 11 Thu Aug 25 Thu Sep 08 Thu Sep 22 Thu Oct 06 Thu Oct 20 Thu Nov 03 Thu Nov 17 Thu Dec 01 Thu Dec 15 Thu Dec 29 Thu Jan 12 Thu Jan 26 Thu Feb 09 Thu Feb 23 Thu Mar 09 Date kWh Mean Median Data: Navetas Loop 15m (2016−06−11 to 2017−03−05, all SAVE data received) Mean of 15 minute intervals
13. 13 Exploring the data Let’s look at this period
14. Exploring the data 14
15. 15 Exploring the data
16. 16 Exploring the data
17. 17 Exploring the data
18. 18 Exploring the data
19. Menu 19 § Data collection – Loop data journey – What else are we collecting? – Constructing the dataset § Exploring the data – Become an energy analyst § What next?
20. What next? More analysis (and data) to understand: § how households respond to different incentives across time § the characteristics of those who respond in the same way § how energy-using habits change 20
21. What next? More data! Using time-use data to better understand changes in energy using practices or routines 21 % point change in timing of reported laundry by weekday vs weekend and by time of day for each year (MTUS UK sub-sample weighted, for clarity, only 95% confidence intervals for Sunday included).[1] 1 Anderson, Ben. ‘Laundry, Energy and Time: Insights from 20 Years of Time-Use Diary Data in the United Kingdom’. Energy Research & Social Science 22 (2016): 125–36. doi:10.1016/j.erss.2016.09.004.
22. Thank you for listening. 22 Photo: ‘Learning’ by CollegeDegrees360 used under Creative Commons License