The Cassandra Platform - Christos Diou

Cassandra platform
Christos Diou
Postdoctoral 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
Cassandra 1st Webinar 2

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!

LIBRARY-BASED SCENARIOS
Cassandra platform – Alpha version

Login screen

Main screen
Projects and entities
Main panel
Libraries

List of projects

User library

Cassandra library

Appliances in Cassandra library

Creating a new project

Adding a new scenario to our project

In this case, Installations are added by drag
n’ drop from the user library

Persons, Activities and Appliances

Activity models

Activity models (duration)

Activity models (start time)

Simulation parameters

Submit runs

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

Behaviour change in Apartment 1

Appliance change in Apartment 3

Submit new runs with same parameters

Compare the runs (graph)

Compare the runs (KPIs)

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

MODEL TRAINING

Training module (to be integrated)

Import installation measurements

Next step: Disaggregation

Training consumer activity models

Export the models to the platform libraries

Models are visible in the user library

CONSUMER RESPONSE

Response models

Modify pricing scheme

Estimate consumer response

Models are posted to the platform

Example 1: Response from

from … to:

Example 2: Response from

from … to:

CONSUMER SOCIAL NETWORKS
(CSN)

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

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

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

The different consumer
groups appear

Another network based on
similar consumption

Change the edge threshold
via the appropriate slider

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

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

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

Thank you!
Questions?

The Cassandra Platform - Christos Diou

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