The document summarizes the Cassandra platform, an open source tool for assessing the impact of strategic energy decisions. It describes several capabilities of the alpha version including pre-existing library components, automatically generating models from measurement data, modeling consumer response, and analyzing consumer social networks. The platform allows simulating scenarios to evaluate solutions before real-life testing and identify optimal consumption schedules. Future work includes a beta release in 2013 with further development of response and social network modules.

1. Cassandra platform
Christos Diou
Kyriakos Chatzidimitriou
Postdoctoral research associates
CERTH/ITI
A multivariate platform for assessing the impact of
strategic decisions in electric power systems
1st CASSANDRA
Workshop
Coventry, 11 September, 2013

2. Outline
• Library-based scenarios in the alpha platform version
– Use pre-existing library components
• Measurement-based scenarios
– Model training to build models automatically
• Response models
– Consumer response to different incentives
• Consumer Social Network analysis
– Grouping of small-scale consumers into Consumer Social Networks
• Development status and next steps

3. About the platform
• Currently in alpha version, development is highly active
– Some functionality has not been integrated yet
• Open source platform, publicly available through GitHub
– http://github.com/cassandra-project
– Apache license 2.0

4. LIBRARY-BASED SCENARIOS
Cassandra platform

5. Login screen

6. Main screen
Main panel
Libraries
Projects and
entities

7. List of projects

8. List of projects

9. User library

10. User library

11. Cassandra library

12. Cassandra library

13. Appliances in Cassandra library

14. Appliances in Cassandra library

15. Creating a new project

16. Creating a new project

17. Adding a new scenario to our project

18. Installations can be added by drag’n’drop
from the user library

19. Persons, Activities, Appliances

20. Persons, Activities, Appliances

21. Activity models

22. Activity models (duration)

23. Activity models (start time)

24. Simulation parameters

25. Simulation parameters

26. Submit runs

27. Submit runs

28. Scenario 1: A mall
• What if …
– Roof-Top-Units (RTUs) are shut down 1 hour prior to mall closing time
taking advantage of thermal inertia in the sales area?
– Gradually start A/C units from 08:00-09:00?
– Set points from 21 to 24 degrees Celsius?
– Shut down all office A/Cs after 22:00 with manual override?
– Set the minimum fresh air from 20% to 5%?
– Change escalators from always on to escalators with motion sensors?

29. Baseline and test-case scenarios - Change a
set point

30. Baseline and test-case scenarios - Change
duration

31. Comparisons

32. SCENARIOS WITH MODEL TRAINING
Cassandra platform

33. Training module

34. Import installation measurements

35. Import installation measurements

36. Next step: Disaggregation

37. Training consumer activity models

38. Training consumer activity models

39. Export the models to the platform libraries

40. Models are visible in the user library

41. CONSUMER RESPONSE
Cassandra platform

42. Response models

43. Modify pricing scheme

44. Estimate consumer response

45. Modify pricing scheme

46. Models are posted to the platform

47. Models are posted to the platform

48. Example 1: Response from … to

49. Example 2: Response from … to
Complete change of habits

50. After 100 Monte Carlo runs of baseline and
response scenarios
…

51. Comparison
of runs

52. CONSUMER SOCIAL NETWORKS
(CSN)
Cassandra platform

53. CSNs
• Groups of similar consumers
– Multiple similarity criteria
• CSNs have potential:
– Increased market power of aggregated small-scale consumers
– Coordination of consumption activities at group level
– Targeted incentives at group level

54. CSN module
• CSN module: A tool for identifying links and grouping of consumers in
a meaningful way
– Existing social network connections
– Explicit attributes (e.g. working, non-working person, locality in the grid
topology)
– Implicit attributes (e.g. consumption similarity, peak similarity, behavioural
similarity)
• Early version implemented for experimentation
• Next version:
– More similarity criteria
– Estimation of group response to incentives
– GUI integration

55. The main graphical interface
of the CSN module

56. The network can be created
based on Installation Type,
Person Type, Average, Peak,
Similar, or Dissimilar
Consumption, etc.

57. CSNetwork based on person
type. Persons of the same
type are linked.

58. A network based on similar
consumption

59. Select a clustering algorithm

60. Adjust the clustering
parameters

61. Clusters appear in different
colors

62. The different consumer
groups appear

63. In summary, with Cassandra you can
• Simulate working scenarios/pilots
• Benchmark different energy efficiency solutions/products in simulation
before testing them in real-life
• Create detailed models that describe consumer behaviour
• Identify and evaluate optimal consumption schedules
• Estimate consumer response to a range of incentives
– Pricing schemes
– Consumer awareness
– Environmental impact
• Identify meaningful consumer groups and benchmark the application
of targeted incentives

64. So, what’s next?
• Beta release is expected before the end of 2013
• Further development and integration of response and CSN modules
• Integration of external modules with the platform
– thermal controllers, lighting models
• Evaluation of Cassandra in our three project pilot cases
• Evaluation of Cassandra in a limited number of NoI pilots (external
evaluation)

65. Thank you!
Questions?