1. Electricity Consumption Power-House Report Presentation -Danielle Jacobs -Callum Bugler -Salaama Maneveld -Clive Mncwabe -Sam Skosana

2. Data collection Current system is tedious and ineffective and not managed effectively. Delays are caused because the collection of data is not collected in a standardised format Often the data is incorrect and/or incomplete Powerstar has proven to be problematic. Situation of Concern

3. Reduce UCT’s expenditure on electricity Through better data recording, there is an opportunity for improved managerial decisions to be made going forward. By lowering electricity usage the university is reducing the core contributor to its carbon footprint. Business Opportunities

4. Await results regarding refitted environmentally friendly equipment as well as add further environmentally additions to the campus Use the IS department and the class of INF3011F to address the problem at hand. Hire external, private sector help. Analysis of Options/Alternative Solutions

5. Improve the current data collection method Calculate UCT’s carbon footprint caused by the scope 2 indirect upstream activity Report fact-based conclusions on findings which are comprehensible and contextual regarding our project. Project Objectives

6. Data Collection DataData formatData HolderComments Main Campus, Satellite Residences, Med Campus and Hiddingh Campus Excel sheet Yusef Davids and Shane Pontes Properties and Services Utilities data used for electricity billing; monthly breakdown. GSBExcel sheetCharlene Paris Figures were given in an excel spreadsheet. Off CampusWebsite to Excel sheetPowerStar Data was transcribed from the website. UCT PopulationExcel sheetRegistrar’s Office This is the total of students registered at UCT for 2014 (31254) and the staff was estimated to 5000. Emission FactorExcel sheetSandra Rippon IS department chose the emission factor based on the GHG protocols recommended for 2014.

7. UCT Per Campus Carbon Emissions for 2014

8. UCT Per Campus Monthly Electricity Consumption for 2014

9. UCT Per Campus Electricity Consumption Comparison for 2013 and 2014

10. Emission Factor Data conversion to CO2 equivalents Comparison of Results Carbon Footprint Calculation Units20132014 Tons CO2-eq71 734 879.40 68 287 772.03

11. Missing buildings on Power star(they are grouped) Some medical school meters not accounted for. Loadshedding late in the year Software malfunction Human error Data Anomalies

12. Occupancy Sensors Solar Heating in Residences Shutting Off Computers After Inactivity Power Down Challenge Electricity Week Recommendations

13. Sustainable Data Collection Recommendations Implement an improved automated system that records energy consumption more efficiently and accurately. Standardize data capture templates across all campuses. Apply built-in conditional formatting and interactive graphs to Excel templates to highlight input errors.

14. Data Capture Template

15. Per Campus Data Capture Template

16. Infographic

17. Incorrect data Delay in receiving accurate data Time slot which suits everyone Load shedding New power star data holder/controller Error on billing data Challenges

18. 3 month time frame Power-Star Anomalies Two source of data Building meters Limitations

19. Main group issue was communication. Used effective techniques which include: – Serious Creativity – Prpic’s model of reflective practice Reflection

20. Questions?