Download Energy Data Management, Bejay Jayan, Cardiff University

Energy Data Management
for Future Cities
Mr. Bejay Jayan
2nd year PhD Researcher
Supervisors : Dr. Haijiang Li & Prof. Yacine Rezgui
Content
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Sustainability Challenges -ENERGY.
Why Buildings.
Saving energy in buildings.
EU Project experience
My research – using ontologies for energy management.
Conclusions
Challenges
• Global energy demand increase by 41 % between 2012 2035. (BP energy outlook)
• Energy production increase proportionally.
• Problem : rising CO2 emissions rate (increase by 29 %
during this period)?
• As a consequence of this : climate change act 2008, 2020
target by EU of generating 15 % electricity from
renewables , strict carbon budgets…etc.
Challenges – What can we do ?
• Increase use of renewables
• Low carbon technologies.
• Save energy whenever/wherever possible through little
steps (management)
Why Buildings ?
• 1/3rd of the global energy consumption.
• Energy production proportional to greenhouse gas
emissions
• In UK : Buildings responsible for 37 % greenhouse gas
emissions.
• Solution : Energy management in BUILDINGS.
Saving energy in Buildings
• Level 1
Ø Take Action based on monitored results .
Ø These systems, nowadays, are also used
to improve energy performance of
buildings and not just for comfort or
security reasons.
• Level 2
Ø Most recent technological developments
based on artificial intelligence techniques
such as neural networks, fuzzy logic, and
genetic algorithms.
?
LEVEL 2 : Neural
Netowrk/Optimisation/
Simulation Models
LEVEL 1 : Building automation
systems (BAS)
Level 2 - EU FP7 project SPORTE2
• Intelligent management system to integrate and control
energy generation, consumption, and exchange for
European Sport and Recreation Buildings.
• Real time energy management.
• Cardiff university involved in optimisation module
development.
Remote Energy Optimization in Sport Facilities
• Scenario: Optimisation of the Air handling units in
swimming pool zone.
• Control parameters: Supplied air flow rate ; supplied air
temperature.
• Objective : Minimise energy consumption ; maintain
Comfort
Remote Energy Optimization in Sport Facilities
Optimum parameters to be
controlled by pilot
Supplied Air flow rate (m3/s)
Air temp. Inlet (deg. C)
obj
Elec_Cons (kwh)
Therm_Eng_Cons (kwh)
PMV
initial
1.6
4.827
5.503
0.036
0.354
5.113
optimum
2.391
9.279
0.105
0.039
0.025
0.042
Sensors relay
information
Stage 2 Optimisation
Stage 1 Artificial Neural Network models
EU FP7 project - SPORTE2
Results removed due to
confidentiality
Why Level 3 ?
Results removed due to
confidentiality
• Scenarios do not consider a holistic viewpoint.
• Solely numerical optimisation.
• The results shown above …. Can we achieve this in reality ?
Saving energy in Buildings
Ontology
Neural
Netowrk/Optimisation/
Simulation Models + BAS
Building automation systems (BAS)
Ontology
• Ontology – a data model that represents knowledge as a set of
concepts within a domain and the relationships between these
concepts.
• Form of knowledge
management.
(Marco Grassi, 2013)
Workflow
Building Automation
systems
Optimisation
ONTOLOGY
Level 3 - Ontology at a Building Level
• Ontologies allow us to see the bigger picture - whole
building context for more efficient energy management in
buildings.
• Add layer of intelligence into traditional optimisation
process through human expertise , or simulation models
or derived from historical data
Level 3 - Ontology at a Building Level – two complementary way
forward to energy saving
Real World
Sensors
Historical
Data
Mining
Database
Sensitivity
Analysis
Scenario
Definition
Energy
Model
Predictive
Rules
Simulation
-based
Rules
Historical Data approach:
• More accurate representation of a
building through its metered data.
• Rules derived from data.
• Rules used through the ontology
for energy saving decision making
Dynamic
Ontology
Real Time Control
and Actuation
Simulation approach:
• Holistic coverage of the building
energy equation.
• Acute understanding of governing
variables and parameters.
Level 3 - Ontology at a DISTRICT LEVEL
Energy Optimization at District Level
Energy Storage
Large Scale Renewable
Energy Generation
Level 3 - Ontology at a DISTRICT LEVEL
OWL/RDF back-office
Ontology management
system
Purpose-built
ontology and
international
standards
District Energy
Visualisation Tool
District editor
Multiagent-based
coordination
District simulation framework
District Energy System
Real-time management framework
Level 3 – Meeting Industry
Requirements
• “We cannot progress fast enough by optimising the city’s
individual components and systems. We need innovation
in integrated and city-wide solutions” (Technology
Strategy Board & Arup,2013)
• “…Over time there will be a large market for integrated
approaches to delivering efficient, attractive and
resilient cities …” (Technology Strategy Board &
Arup,2013)
• “CO2 emissions is a systematic problem” (VOcamp ,2014)
Conclusions
• Holistic approach to Energy management needs to be
given importance to tackle CO2 emissions.
• Ontologies make traditional optimisation RICH.
• Energy management in Buildings >>>> Districts>>>>
Future cities.
Thank you for listening.
Acknowledgements
Special thanks to SPORTE2 project partners for the research
efforts and for contributing to some of the content mentioned in
this presentation.
Mr. Bejay Jayan
[email protected]
+ 447595701431
School of Engineering
Cardiff University