smartbuildings: an ambient intelligence system for energy ... · motivations smartbuildings: an ami...

SmartBuildings: an Ambient Intelligence System for Energy EfficiencyGiuseppe Lo Re, Marco Ortolani, Marco Morana, Alessandra De PaolaUniversity of Palermo, Italy

Motivations

SmartBuildings: an AmI System for Energy Efficiency

�  Smart Cities composed of efficient urban elements ¡  Energy, Transportation, Buildings

�  Buildings are responsible for 40% of energy consumption and 36% of CO2 emissions in the EU [European Commission, 2015].

�  Old buildings consume 6-8 times more than newer ones.

�  Currently, about 35% of the EU's buildings are over 50 years old.

�  ICT for reducing energy consumption of pre-existing buildings

Goals


�  Ambient Intelligence (AmI) for developing Smart Buildings ¡  Pervasive monitoring equipment ¡  Responsiveness to the users’ needs ¡  Respectful of energy saving requirements

�  Smart Cities composed of Smart Buildings ¡  User communicates with Smart Buildings through mobile

devices ¡  Smart Buildings communicate each other to improve energy

efficiency and exchange services

Past Experiences


�  Wireless Sensor and Actuator Networks (WSANs) extend the functionalities of traditional WSNs by adding control devices, i.e., actuators.

�  Artificial Intelligence approaches for optimizing the energy efficiency and satisfying user’s needs

�  Research funded by ¡  POR FESR SICILIA 2007-2013, “SmartBuildings” for the realization

of an intelligent system for ubiquitous observations of pre-existing buildings.

¡  PON R&C Industria 2015, “SeNSori” for the definition of an Ambient Intelligence architecture for integrating sensors via a Plug&Play middleware

SmartBuildings Architecture


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�  Three-Layers architecture

�  From raw data to abstract concepts

SmartBuildings Architecture - Physical Layer


�  Heterogeneous sensor network ¡  Environmental

conditions ÷ CO2/Temp/

Hum/Light ¡  Power

Consumption ¡  User Actions

�  Actuators ¡  HVAC ¡  Light ¡  Windows

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Pervasive & distributed sensor & actuator

networks

WSN

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Case Study – Campus

SENSORS ACTUATORS



SENSORS

CO2/Temp/Hum/Light

ACTUATORS



SENSORS

¢ CO2/Temp/Hum/Light u SW

ACTUATORS



SENSORS


¤ Power consumption

ACTUATORS

¤

¤

¤ ¤ ¤



SENSORS


¤ Power consumption n RFID

ACTUATORS



SENSORS

¢ CO2/Temp/Hum/Light u SW � Kinect


ACTUATORS

�



� HVAC

ACTUATORS SENSORS



�

�

�



� HVAC � Rolling shutters/curtains

ACTUATORS SENSORS



� � �

�



� HVAC � Rolling shutters/curtains � Light

ACTUATORS SENSORS



� � �

� � �

�

�

SmartBuildings Architecture - Middelware Layer


�  AmI building blocks ¡  Prediction of

how the observed physical quantities change over time ÷ Exploits past

and current measurements

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networks

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Environmental Predictor



�  AmI building blocks ¡  Environmental

Prediction ¡  User activity

recognition ÷ Hidden Markov

Model ÷ Heterogeneous

sensors

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networks

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Activity Recognizer



�  AmI building blocks ¡  Environmental ¡  User activity

recognition ¡  Learning user

preferences ÷  Implicit and

explicit feedback ÷ Reinforcement

Learning

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networks

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Activity Recognizer Profiler

SmartBuildings Architecture - Application Layer


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networks

�  Hybrid Intelligence ¡  Deliberative

Intelligence ÷  Daily planning ÷  Find best action

rules for Reactive Intelligence

¡  Reactive Intelligence ÷  Fast reacts to

environment changes

WSN

…..


Activity Recognizer Profiler

Deliberative Intelligence

Reactive Intelligence

Deliberative and Reactive Intelligence


�  Exploits output of AmI modules

ENVIRONMENTAL PREDICTOR

ACTIVITY RECOGNIZER PROFILER

DELIBERATIVE REACTIVE



�  Deliberative Intelligence: Daily planning (best action rules)




TABU SEARCH

FUZZY RULES



�  Reactive Intelligence: Fast reacts to environment changes



PLANNER


FUZZY CONTROL

TABU SEARCH

FUZZY RULES





PLANNER


FUZZY CONTROL

TABU SEARCH

FUZZY RULES

IF Ti HIGH & Te HIGH & Hum LOW & A == MEETING THEN SET HVAC COOL, SET TEMP NORMAL COMMAND : [COOL, 24 °C]

Main Research Results


�  Intelligent Management Systems for Energy Efficiency in Buildings: A Survey. A. De Paola, M. Ortolani, G. Lo Re, G. Anastasi, S.K. Das. ACM Computing Surveys, 2015

�  Adaptive Distributed Outlier Detection for WSNs. A. De Paola, S. Gaglio, G. Lo Re, F. Milazzo, M. Ortolani. In IEEE Transactions on Cybernetics, 2014

�  Sensor 9k: A testbed for designing and experimenting with WSN-based ambient intelligence applications. A. De Paola, S. Gaglio, G. Lo Re, M. Ortolani. In Pervasive and Mobile Computing, 2012

Smart Cities: communities of SmartBuildings


�  Smart Buildings capable of communicating each other

�  Cooperatively refine their energy saving strategies

Internet





�  New buildings may exploit past experience of other buildings ¡  Adopt strategy already proved to be efficient

¡  Learned model for actuator effect on the environment

¡  Learned model of user behavior and preferences





�  New buildings may exploit past experience of other buildings ¡  Adopt strategy already proved to be efficient

¡  Learned model for actuator effect on the environment

¡  Learned model of user behavior and preferences

�  Distributed optimization for meeting common constraints and goals



�  Technological issues: Security ¡  Sensitive information can be intercepted by non authorized users



�  Technological issues: Security ¡  Sensitive information can be intercepted by non authorized users

¡  Malicious users can introduce false information into the system and compromise system reliability



�  Technological issues: Knowledge abstraction ¡  Buildings can have heterogeneous structures and equipment




¡  Buildings can adopt different tecnhological approach (e.g. fuzzy controller, Bayesian decision network)

FUZZY CONTROL




¡  Buildings can adopt different tecnhological approach (e.g. fuzzy controller, Bayesian decision network)

¡  How to represent energy strategies?

¡  How adapt models learned by other buildings?



�  Technological issues: Interaction with Users ¡  Users communicate remotely with buildings via mobile devices



�  Technological issues: Interaction with Users ¡  Users communicate remotely with buildings via mobile devices

¡  Privacy issues: identity privacy, location privacy

References


�  SmartBuildings: an AmI System for Energy Efficiency. A. De Paola, G. Lo Re, M. Morana, M. Ortolani. In Proceedings of the 4th International Conference on Sustainable Internet and ICT for Sustainability, 2015

�  Human Activity Recognition Process Using 3-D Posture Data. S. Gaglio, G. Lo Re, M. Morana. In Human-Machine Systems, IEEE Transactions on, vol.45, no.5, pp.586-597, 2015

�  Autonomic Behaviors in an Ambient Intelligence System. A. De Paola, P. Ferraro, S. Gaglio, G. Lo Re. In Proceedings of the 2014 IEEE Symposium on Computational Intelligence for Human-like Intelligence (IEEE SSCI 2014)

�  User activity recognition for energy saving in smart homes. P. Cottone, S. Gaglio, G. Lo Re, M. Ortolani. In Journal of Pervasive and Mobile Computing, Volume 16, Issue PA, 2015, Pages 156-170

�  Sensor Networks for Energy Sustainability in Buildings. A. De Paola, M. Ortolani, G. Lo Re, G. Anastasi, S.K. Das. In Sensor Networks for Sustainable Development, 2014, pp. 107-122, ISBN: 978-1-4665-8206-4, DOI: 10.1201/b17124-10

�  Intelligent Management Systems for Energy Efficiency in Buildings: A Survey. A. De Paola, M. Ortolani, G. Lo Re, G. Anastasi, S.K. Das. ACM Computing Surveys, Vol. 47,n. 1, 2014, Article a13

�  User Activity Recognition via Kinect in an Ambient Intelligence Scenario. P. Cottone, G. Maida, M. Morana. In Proceedings of 2013 International Conference on Applied Computing, Computer Science, and Computer Engineering, 2013

�  User Activity Recognition for Energy Saving in Smart Homes. P. Cottone, S. Gaglio, G. Lo Re, M. Ortolani. In Proceedings of the 3rd International Conference on Sustainable Internet and ICT for Sustainability, 2013, pp. 1-9

�  Bio-inspired Sensory Data Aggregation. A. De Paola, M. Morana. In Biologically Inspired Cognitive Architectures, 2012, pp. 367-368.

References


�  Improving User Experience via Motion Sensors in an Ambient Intelligence Scenario. G. Lo Re, M. Morana, M. Ortolani. In Proceedings of the 3rd International Conference on Pervasive and Embedded Computing and Communication Systems, 2013

�  Motion Sensors for Activity Recognition in an Ambient-Intelligence Scenario. P. Cottone, G. Lo Re, G. Maida, M. Morana. In Proocedings of the 5th International Workshop on Smart Environments and Ambient Intelligence, 2013, pp. 646-651

�  Mimicking biological mechanisms for sensory information fusion. A. De Paola, M. La Cascia, G. Lo Re, M. Morana, M. Ortolani. In Journal of Biologically Inspired Cognitive Architectures, vol. 3, 2013, pp. 27-38.

�  An intelligent system for energy efficiency in a complex of buildings. A. De Paola, G. Lo Re, M. Morana, M. Ortolani. In Proceedings of the 2nd International Conference on Sustainable Internet and ICT for Sustainability, 2012, pp. 1-5

�  Sensor 9k: A testbed for designing and experimenting with WSN-based ambient intelligence applications. A. De Paola, S. Gaglio, G. Lo Re, M. Ortolani. In Pervasive and Mobile Computing, vol. 8, issue 3, 2012, pp. 448-466

�  A Distributed Bayesian Approach to Fault Detection in Sensor Networks. G. Lo Re, F. Milazzo, M. Ortolani. In Proceedings of the IEEE Global Telecommunications Conference (GlobeCom), 2012, pp. 634-639

�  User detection through multi-sensor fusion in an AmI scenario. A. De Paola, M. La Cascia, G. Lo Re, M. Morana, M. Ortolani. In Proceedings of the 15th International Conference on Information Fusion, 2012, pp. 2502-2509

smartbuildings: an ambient intelligence system for energy ... · motivations smartbuildings: an ami...

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