the cassandra platform - christos diou
TRANSCRIPT
Cassandra platform
Christos DiouPostdoctoral Researcher
Information Technologies Institute
(CERTH-ITI)
Cassandra platform – Christos Diou
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
• NoI members and the platform
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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
– Apache license
• Your feedback is highly appreciated!
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LIBRARY-BASED SCENARIOSCassandra platform – Alpha version
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Cassandra platform – Christos Diou
Login screen
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Main screen
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Projects and entities
Main panel
Libraries
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List of projects
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User library
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Cassandra library
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Appliances in Cassandra library
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Creating a new project
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Adding a new scenario to our project
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In this case, Installations are added by drag n’ drop from the user library
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Persons, Activities and Appliances
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Activity models
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Activity models (duration)
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Activity models (start time)
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Simulation parameters
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Submit runs
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What if…• The residents of Apartment 1 use the water heater less
– Because they have installed solar water heating
• The residents of Apartment 2 use their air conditioning unit less
– Because they have upgraded their apartment’s thermal insulation properties
• The residents of Apartment 3 replace their refrigeration unit
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Behaviour change in Apartment 1
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Behaviour change in Apartment 1
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Behaviour change in Apartment 2
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Behaviour change in Apartment 2
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Appliance change in Apartment 3
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Appliance change in Apartment 3
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Submit new runs with same parameters
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Cassandra platform – Christos Diou
Compare the runs (graph)
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Compare the runs (KPIs)
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One step further
• Library-based scenarios can be used to simulate working hypotheses
• The real power of Cassandra is in its disaggregation and model training functionality
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MODEL TRAININGCassandra platform – Alpha version
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Training module (to be integrated)
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Import installation measurements
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Import installation measurements
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Next step: Disaggregation
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Training consumer activity models
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Training consumer activity models
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Export the models to the platform libraries
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Models are visible in the user library
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CONSUMER RESPONSECassandra platform – Alpha version
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Response models
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Modify pricing scheme
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Estimate consumer response
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Models are posted to the platform
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Models are posted to the platform
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Example 1: Response from
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from … to:
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Example 2: Response from
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from … to:
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CONSUMER SOCIAL NETWORKS (CSN)
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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
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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
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The main graphical interface of the CSN module
The network can be created based on Installation Type,
Person Type, Average, Peak, Similar, or Dissimilar Consumption, etc.
CSNetwork based on person type. Persons of the same
type are linked.
By changing the visualization layout algorithm….
The two different person types appear
Visualization is Important!!!
A new network based on Peak Consumption similarity
We select the clustering algorithm to use
Clusters appear in different colours
By grouping clusters…
The different consumer groups appear
Another network based on similar consumption
Change the edge threshold via the appropriate slider
Select a clustering algorithm
Clusters appear in different colors
Various statistics are available, as well as the
consumption and information of selected nodes
Sliders for minimum edge weight and clustering values
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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
Cassandra platform – Christos Diou
So, what’s next?
• Beta release is planned for October, 2013
• Integration of external modules with the platform
• Evaluation of Cassandra in our three project pilot cases
• Evaluation of Cassandra in a limited number of NoI pilots (external evaluation)
• We can use Cassandra to simulate your context and benchmark a number of working scenarios.
– Use measurements and the training module to model your context
– Simulate a range of scenarios appropriate for your business case
– Prepare an analysis/report of our findings
– Ask for your feedback
– Improve the platform based on your comments
Cassandra platform – Christos Diou
Thank you!
Questions?