Employing Computational Intelligence to Generate More Intelligent and Energy Efficient Living Spaces

doi:10.1007/s11633-008-0001-7

January 2008

Article Contents

Employing Computational Intelligence to Generate More Intelligent and Energy Efficient Living Spaces. International Journal of Automation and Computing, vol. 5, no. 1, pp. 1-9, 2008. doi: 10.1007/s11633-008-0001-7

Cite as: Employing Computational Intelligence to Generate More Intelligent and Energy Efficient Living Spaces. International Journal of Automation and Computing, vol. 5, no. 1, pp. 1-9, 2008. doi: 10.1007/s11633-008-0001-7

Employing Computational Intelligence to Generate More Intelligent and Energy Efficient Living Spaces

1.
Department of Computing and Electronic Systems, Centre for Computational Intelligence, University of Essex, Wivenhoe Park, Colchester CO43SQ, UK

Received: 2007-08-22

Abstract

Our living environments are being gradually occupied with an abundant number of digital objects that have networking and computing capabilities.After these devices are plugged into a network,they initially advertise their presence and capabilities in the form of services so that they can be discovered and,if desired,exploited by the user or other networked devices.With the increasing number of these devices attached to the network,the complexity to configure and control them increases,which may lead to major processing and communication overhead.Hence,the devices are no longer expected to just act as primitive stand-alone appliances that only provide the facilities and services to the user they are designed for,but also offer complex services that emerge from unique combinations of devices.This creates the necessity for these devices to be equipped with some sort of intelligence and self-awareness to enable them to be self-configuring and self-programming.However,with thissmart evolution,the cognitive load to configure and control such spaces becomes immense.One way to relieve this load is by employing artificial intelligence(AI)techniques to create an intelligentpresencewhere the system will be able to recognize the users and autonomously program the environment to be energy efficient and responsive to the users needs and behaviours.These AI mechanisms should be embedded in the users environments and should operate in a non-intrusive manner.This paper will show how computational intelligence(CI),which is an emerging domain of AI,could be employed and embedded in our living spaces to help such environments to be more energy efficient,intelligent,adaptive and convenient to the users.
- Computational intelligence(CI),
- fuzzy systems,
- neural networks(NNs),
- genetic algorithms(GAs),
- intelligent buildings,
- energy efficiency

References

[1]	M.Weiser.The Computer for the 21st Century.Scientific American,vol.265,no.3,pp.94-104,1991.
[2]	R.Want,G.Borriello,T.Pering,K.I.Farkas.Disappearing Hardware.IEEE Pervasive Computing,vol.1,no.1,pp.36-47,2002.
[3]	S.Haykin.Neural Networks:A Comprehensive Foundation,Prentice Hall,Upper Saddle River,New Jersey,USA,1994.
[4]	M.Bailey,P.Curtiss.Neural Network Modeling and Control Applications in Building Mechanical Systems.In Proceedings of International Conference of Chartered Institution of Building Services Engineers and American Society of Heating Refrigeration and Air-conditioning Engineers,London,England,2001,[Online] ,Avaiable:http://www.cibse.org/pdfs/neural2.pdf,2001.
[5]	S.A.Kalogirou.Artificial Neural Networks in Renewable Energy Systems Applications:A Review.Renewable and Sustainable Energy Reviews,vol.5,no.4,pp.373-401,2001.
[6]	C.W.Anderson,D.C.Hittle,A.D.Katz,P.M.Kretchmar.Synthesis of Reinforcement Learning,Neural Networks,and PI Control Applied to a Simulated Heating Coil.Journal of Artificial Intelligence in Engineering,vol.11,no.4,pp.421-429,1997.
[7]	D.Cohen,M.Krarti.A Neural Network Modeling Approach Applied to Energy Conservation Retrofits.In Proceedings of International Conference on Building Simulation,Madison,Wisconsin,USA,pp.423-430,1995.
[8]	J.F.Kreider,X.A.Wang.Artificial Neural Network Demonstration for Automated Generation of Energy Use Predictors for Commercial Buildings.ASHRAE Transactions,vol.91,no.1,pp.775-779,1995.
[9]	M.Mozer.Lessons from an Adaptive Home.Smart Environments:Technology,Protocols,and Applications,D.J.Cook,S.K.Das (eds.),John Wiley & Sons,New Jersey,USA,pp.273-298,2005.
[10]	F.Rivera-Illingworth,V.Callaghan,H.Hagras.Automated Discovery of Human Activites inside Pervasive Living Spaces.In Proceedings of the International Symposium on Pervasive Computing and Applications,Urumchi,Xinjiang,China,pp.77-82,2006.
[11]	H.Hagras,F.Doctor,V.Callaghan,A.Lopez.An Incremental Adaptive Life Long Learning Approach for Type-2 Fuzzy Embedded Agents in Ambient Intelligent Environments.IEEE Transactions on Fuzzy Systems,vol.15,no.1,pp.41-55,2007.
[12]	A.L.Dexter,M.Benouartes.Model Based Fault Diagnosis Using Fuzzy Matching.IEEE Transactions on Systems,Man,and Cybernetics -Part A,vol.27,no.5,pp.673-682,1997.
[13]	D.Kolokotsa,G.Stavrakakis,K.Kalaitzakis,D.Tsiavos.Fuzzy Control for Improved Buildings Energy Management Systems.In Proceedings of the European Symposium on Intelligent Techniques,Crete,Greece,1999,[Online] ,Available:http://www.erudit.de/erudit/events/esit99/programme.htm,June 3,1999.
[14]	B.Egilegor,P.Uribe,G.Arregi,E.Pradilla,L.Susperregi.A Fuzzy Control Adapted by a Neural Network to Maintain a Dwelling within Thermal Comfort.In Proceedings of Building Simulation,Prague,Czech Republic,vol.2,pp.87-94,1997.
[15]	S.Kajl,M.A.Roberge,L.Lamarche,P.Malinowski.Evaluation of Building Energy Consumption Based on Fuzzy Logic and Neural Networks Applications.In Proceedings of CLIMA 2000 Conference,Brussels,Belgium,1997,[Online] ,Available:http://www.inive.org/members_area/medias/pdf/Inive/clima2000/1997/P264.pdf,August,1997.
[16]	U.Rutishauser,J.Joller,R.Douglas.Control and Learning of Ambience by an Intelligent Building.IEEE Transactions on Systems,Man,and Cybernetics-Part A:Systems and Humans,vol.35,no.1,pp.121-132,2005.
[17]	F.Doctor,H.Hagras,V.Callaghan.An Intelligent Fuzzy Agent Approach for Realising Ambient Intelligence in Intelligent Inhabited Environments.IEEE Transactions on Systems,Man,and Cybernetics -Part A:Systems and Humans,vol.35,no.1,pp.55-65,2005.
[18]	H.N.Lam.Stochastic Modelling and Genetic Algorithmbased Optimal Control of Air Conditioning Systems.In Proceedings of the 3rd International Conference of the International Building Performance Simulation Association,Adelaide,Australia,pp.435-441,1993.
[19]	H.N.Lam.Intelligent Computer Control of Air Conditioning Systems Based on Genetic Algorithm and Classifier System.In Proceedings of the 4th International Conference of the International Building Performance Simulation Association on Building Simulation,Madison,Wisconsin,USA,pp.151-157,1995.

Metrics

[1]	Yue Wu, Jun-Wei Liu, Chen-Zhuo Zhu, Zhuang-Fei Bai, Qi-Guang Miao, Wen-Ping Ma, Mao-Guo Gong. Computational Intelligence in Remote Sensing Image Registration: A survey . International Journal of Automation and Computing, 2021, 18(1): 1-17. doi: 10.1007/s11633-020-1248-x
[2]	Farzam Matinfar. A Computational Model for Measuring Trust in Mobile Social Networks Using Fuzzy Logic . International Journal of Automation and Computing, 2020, 17(6): 812-821. doi: 10.1007/s11633-020-1232-5
[3]	Hussein El Ghor, El-Hadi M. Aggoune. Energy Efficient Scheduler of Aperiodic Jobs for Real-time Embedded Systems . International Journal of Automation and Computing, 2020, 17(5): 733-743. doi: 10.1007/s11633-016-0993-3
[4]	Hussein El Ghor, Maryline Chetto. Energy Guarantee Scheme for Real-time Systems with Energy Harvesting Constraints . International Journal of Automation and Computing, 2019, 16(3): 354-368. doi: 10.1007/s11633-018-1166-3
[5]	Yu Zhang, Chris Bingham, Mike Garlick, Michael Gallimore. Applied Fault Detection and Diagnosis for Industrial Gas Turbine Systems . International Journal of Automation and Computing, 2017, 14(4): 463-473. doi: 10.1007/s11633-016-0967-5
[6]	Imad Benacer, Zohir Dibi. Extracting Parameters of OFET Before and After Threshold Voltage Using Genetic Algorithms . International Journal of Automation and Computing, 2016, 13(4): 382-391. doi: 10.1007/s11633-015-0918-6
[7]	Khalid Jebari, Abdelaziz Bouroumi, Aziz Ettouhami. Fuzzy Genetic Sharing for Dynamic Optimization . International Journal of Automation and Computing, 2012, 9(6): 616-626 . doi: 10.1007/s11633-012-0687-4
[8]	Ahmad T. Abdulsadda, Kamran Iqbal. An Improved SPSA Algorithm for System Identification Using Fuzzy Rules for Training Neural Networks . International Journal of Automation and Computing, 2011, 8(3): 333-339. doi: 10.1007/s11633-011-0589-x
[9]	Raed I. Hamed, Syed I. Ahson, Rafat Parveen. Designing Genetic Regulatory Networks Using Fuzzy Petri Nets Approach . International Journal of Automation and Computing, 2010, 7(3): 403-412. doi: 10.1007/s11633-010-0521-9
[10]	Hoang Pham, Hoang Pham Jr.. Improving Energy and Power Efficiency Using NComputing and Approaches for Predicting Reliability of Complex Computing Systems . International Journal of Automation and Computing, 2010, 7(2): 153-159. doi: 10.1007/s11633-010-0153-0
[11]	Shao-Cheng Tong, Yong-Ming Li. Adaptive Backstepping Output Feedback Control for SISO Nonlinear System Using Fuzzy Neural Networks . International Journal of Automation and Computing, 2009, 6(2): 145-153. doi: 10.1007/s11633-009-0145-0
[12]	Zhi-Le Xia, Jun-Min Li. GH₂ Control for Uncertain Discrete-time-delay Fuzzy Systems Based on a Switching Fuzzy Model and Piecewise Lyapunov Function . International Journal of Automation and Computing, 2009, 6(3): 261-266. doi: 10.1007/s11633-009-0261-x
[13]	. Computational Intelligence Determines Effective Rationality . International Journal of Automation and Computing, 2008, 5(1): 63-66. doi: 10.1007/s11633-008-0063-6
[14]	. Computational Intelligence and Games:Challenges and Opportunities . International Journal of Automation and Computing, 2008, 5(1): 45-57. doi: 10.1007/s11633-008-0045-8
[15]	James M. Gilbert, Farooq Balouchi. Comparison of Energy Harvesting Systems for Wireless Sensor Networks . International Journal of Automation and Computing, 2008, 5(4): 334-347. doi: 10.1007/s11633-008-0334-2
[16]	P. K. Kapur, Anshu Gupta, P. C. Jha. Reliability Growth Modeling and Optimal Release Policy Under Fuzzy Environment of an N-version Programming System Incorporating the Effect of Fault Removal Efficiency . International Journal of Automation and Computing, 2007, 4(4): 369-379. doi: 10.1007/s11633-007-0369-9
[17]	Shengxiang Yang, Renato Tinós. A Hybrid Immigrants Scheme for Genetic Algorithms in Dynamic Environments . International Journal of Automation and Computing, 2007, 4(3): 243-254. doi: 10.1007/s11633-007-0243-9
[18]	Sing Kiong Nguang, Ping Zhang, Steven X. Ding. Parity Relation Based Fault Estimation for Nonlinear Systems: An LMI Approach . International Journal of Automation and Computing, 2007, 4(2): 164-168. doi: 10.1007/s11633-007-0164-7
[19]	Marek Kowal, Józef Korbicz. Fault Detection under Fuzzy Model Uncertainty . International Journal of Automation and Computing, 2007, 4(2): 117-124. doi: 10.1007/s11633-007-0117-1
[20]	N. Eva Wu, Sudha Thavamani, Xiaohua Li. Reliability and Feedback of Multiple Hop Wireless Networks . International Journal of Automation and Computing, 2007, 4(2): 125-134. doi: 10.1007/s11633-007-0125-1

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Cite This Article

PDF

XML

Entire Issue PDF

用微信扫码二维码

分享至好友和朋友圈

Metrics

Abstract Views (4398) PDF downloads (2386) Citations (0)

Related Articles

Employing Computational Intelligence to Generate More Intelligent and Energy Efficient Living Spaces

1. Department of Computing and Electronic Systems, Centre for Computational Intelligence, University of Essex, Wivenhoe Park, Colchester CO43SQ, UK

Received: 2007-08-22

Key words:

Abstract: Our living environments are being gradually occupied with an abundant number of digital objects that have networking and computing capabilities.After these devices are plugged into a network,they initially advertise their presence and capabilities in the form of services so that they can be discovered and,if desired,exploited by the user or other networked devices.With the increasing number of these devices attached to the network,the complexity to configure and control them increases,which may lead to major processing and communication overhead.Hence,the devices are no longer expected to just act as primitive stand-alone appliances that only provide the facilities and services to the user they are designed for,but also offer complex services that emerge from unique combinations of devices.This creates the necessity for these devices to be equipped with some sort of intelligence and self-awareness to enable them to be self-configuring and self-programming.However,with thissmart evolution,the cognitive load to configure and control such spaces becomes immense.One way to relieve this load is by employing artificial intelligence(AI)techniques to create an intelligentpresencewhere the system will be able to recognize the users and autonomously program the environment to be energy efficient and responsive to the users needs and behaviours.These AI mechanisms should be embedded in the users environments and should operate in a non-intrusive manner.This paper will show how computational intelligence(CI),which is an emerging domain of AI,could be employed and embedded in our living spaces to help such environments to be more energy efficient,intelligent,adaptive and convenient to the users.

HTML

Reference (19)

[1]	M.Weiser.The Computer for the 21st Century.Scientific American,vol.265,no.3,pp.94-104,1991.
[2]	R.Want,G.Borriello,T.Pering,K.I.Farkas.Disappearing Hardware.IEEE Pervasive Computing,vol.1,no.1,pp.36-47,2002.
[3]	S.Haykin.Neural Networks:A Comprehensive Foundation,Prentice Hall,Upper Saddle River,New Jersey,USA,1994.
[4]	M.Bailey,P.Curtiss.Neural Network Modeling and Control Applications in Building Mechanical Systems.In Proceedings of International Conference of Chartered Institution of Building Services Engineers and American Society of Heating Refrigeration and Air-conditioning Engineers,London,England,2001,[Online] ,Avaiable:http://www.cibse.org/pdfs/neural2.pdf,2001.
[5]	S.A.Kalogirou.Artificial Neural Networks in Renewable Energy Systems Applications:A Review.Renewable and Sustainable Energy Reviews,vol.5,no.4,pp.373-401,2001.
[6]	C.W.Anderson,D.C.Hittle,A.D.Katz,P.M.Kretchmar.Synthesis of Reinforcement Learning,Neural Networks,and PI Control Applied to a Simulated Heating Coil.Journal of Artificial Intelligence in Engineering,vol.11,no.4,pp.421-429,1997.
[7]	D.Cohen,M.Krarti.A Neural Network Modeling Approach Applied to Energy Conservation Retrofits.In Proceedings of International Conference on Building Simulation,Madison,Wisconsin,USA,pp.423-430,1995.
[8]	J.F.Kreider,X.A.Wang.Artificial Neural Network Demonstration for Automated Generation of Energy Use Predictors for Commercial Buildings.ASHRAE Transactions,vol.91,no.1,pp.775-779,1995.
[9]	M.Mozer.Lessons from an Adaptive Home.Smart Environments:Technology,Protocols,and Applications,D.J.Cook,S.K.Das (eds.),John Wiley & Sons,New Jersey,USA,pp.273-298,2005.
[10]	F.Rivera-Illingworth,V.Callaghan,H.Hagras.Automated Discovery of Human Activites inside Pervasive Living Spaces.In Proceedings of the International Symposium on Pervasive Computing and Applications,Urumchi,Xinjiang,China,pp.77-82,2006.
[11]	H.Hagras,F.Doctor,V.Callaghan,A.Lopez.An Incremental Adaptive Life Long Learning Approach for Type-2 Fuzzy Embedded Agents in Ambient Intelligent Environments.IEEE Transactions on Fuzzy Systems,vol.15,no.1,pp.41-55,2007.
[12]	A.L.Dexter,M.Benouartes.Model Based Fault Diagnosis Using Fuzzy Matching.IEEE Transactions on Systems,Man,and Cybernetics -Part A,vol.27,no.5,pp.673-682,1997.
[13]	D.Kolokotsa,G.Stavrakakis,K.Kalaitzakis,D.Tsiavos.Fuzzy Control for Improved Buildings Energy Management Systems.In Proceedings of the European Symposium on Intelligent Techniques,Crete,Greece,1999,[Online] ,Available:http://www.erudit.de/erudit/events/esit99/programme.htm,June 3,1999.
[14]	B.Egilegor,P.Uribe,G.Arregi,E.Pradilla,L.Susperregi.A Fuzzy Control Adapted by a Neural Network to Maintain a Dwelling within Thermal Comfort.In Proceedings of Building Simulation,Prague,Czech Republic,vol.2,pp.87-94,1997.
[15]	S.Kajl,M.A.Roberge,L.Lamarche,P.Malinowski.Evaluation of Building Energy Consumption Based on Fuzzy Logic and Neural Networks Applications.In Proceedings of CLIMA 2000 Conference,Brussels,Belgium,1997,[Online] ,Available:http://www.inive.org/members_area/medias/pdf/Inive/clima2000/1997/P264.pdf,August,1997.
[16]	U.Rutishauser,J.Joller,R.Douglas.Control and Learning of Ambience by an Intelligent Building.IEEE Transactions on Systems,Man,and Cybernetics-Part A:Systems and Humans,vol.35,no.1,pp.121-132,2005.
[17]	F.Doctor,H.Hagras,V.Callaghan.An Intelligent Fuzzy Agent Approach for Realising Ambient Intelligence in Intelligent Inhabited Environments.IEEE Transactions on Systems,Man,and Cybernetics -Part A:Systems and Humans,vol.35,no.1,pp.55-65,2005.
[18]	H.N.Lam.Stochastic Modelling and Genetic Algorithmbased Optimal Control of Air Conditioning Systems.In Proceedings of the 3rd International Conference of the International Building Performance Simulation Association,Adelaide,Australia,pp.435-441,1993.
[19]	H.N.Lam.Intelligent Computer Control of Air Conditioning Systems Based on Genetic Algorithm and Classifier System.In Proceedings of the 4th International Conference of the International Building Performance Simulation Association on Building Simulation,Madison,Wisconsin,USA,pp.151-157,1995.

Employing Computational Intelligence to Generate More Intelligent and Energy Efficient Living Spaces

Abstract

References

Metrics

Related Articles

通讯作者: 陈斌, bchen63@163.com

Metrics