skip to main content
research-article

Intelligent Management Systems for Energy Efficiency in Buildings: A Survey

Published: 01 June 2014 Publication History

Abstract

In recent years, reduction of energy consumption in buildings has increasingly gained interest among researchers mainly due to practical reasons, such as economic advantages and long-term environmental sustainability. Many solutions have been proposed in the literature to address this important issue from complementary perspectives, which are often hard to capture in a comprehensive manner. This survey article aims at providing a structured and unifying treatment of the existing literature on intelligent energy management systems in buildings, with a distinct focus on available architectures and methodology supporting a vision transcending the well-established smart home vision, in favor of the novel Ambient Intelligence paradigm. Our exposition will cover the main architectural components of such systems, beginning with the basic sensory infrastructure, moving on to the data processing engine where energy-saving strategies may be enacted, to the user interaction interface subsystem, and finally to the actuation infrastructure necessary to transfer the planned modifications to the environment. For each component, we will analyze different solutions, and we will provide qualitative comparisons, also highlighting the impact that a single design choice can have on the rest of the system.

Supplementary Material

a13-paola-apndx.pdf (paola.zip)
Supplemental movie, appendix, image and software files for, Intelligent Management Systems for Energy Efficiency in Buildings: A Survey

References

[1]
Y. Agarwal, B. Balaji, R. Gupta, J. Lyles, M. Wei, and T. Weng. 2010. Occupancy-driven energy management for smart building automation. In Proc. of the 2nd ACM Work. on Embedded Sensing Syst. for Energy-Efficiency in Building (BuildSys’10). 1--6.
[2]
Y. Agarwal, T. Weng, and R. K. Gupta. 2009. The energy dashboard: Improving the visibility of energy consumption at a campus-wide scale. In Proc. of the 1st ACM Work. on Embedded Sensing Syst. for Energy-Efficiency in Buildings (BuildSys’09). 55--60.
[3]
AlertMe. 2013. (2013). Homepage. Available at http://www.alertme.com/.
[4]
G. Anastasi, M. Conti, M. Di Francesco, and A. Passarella. 2009. Energy conservation in wireless sensor networks: A survey. Ad Hoc Networks 7, 3 (2009), 537--568.
[5]
Ashrae Standards. 2013. Homepage. Available athttp://www.ashrae.org/standards-research--technology/standards--guidelines.
[6]
S. Attia, L. Beltrán, A. De Herde, and J. Hensen. 2009. Architect Friendly: A comparison of ten different building performance simulation tools. In Proc. of the 11th Int. IBPSA Conf. 204--211.
[7]
M. Baranski and J. Voss. 2004. Genetic algorithm for pattern detection in NIALM systems. In Proc. of the 2004 IEEE Int. Conf. on Syst., Man, Cybern., Vol. 4. 3462--3468.
[8]
C. Beckel, L. Sadamori, and S. Santini. 2013. Automatic socio-economic classification of households using electricity consumption data. In Proc. of the 4th Int. Conf. on Future energy Systems. 75--86.
[9]
L. Benini, E. Farella, and C. Guiducci. 2006. Wireless sensor networks: Enabling technology for ambient intelligence. Microelectronics Journal 37, 12 (2006), 1639--1649.
[10]
V. Boton-Fernandez and A. Lozano-Tello. 2011. Learning Algorithm for Human Activity Detection in Smart Environments. In Proc. of the 2011 IEEE/WIC/ACM Int. Conf. on Web Intelligence and Intelligent Agent Technology (WI-IAT’11), Vol. 3. 45--48.
[11]
R. Campbell, J. Al-Muhtadi, P. Naldurg, G. Sampemane, and M. D. Mickunas. 2002. Towards security and privacy for pervasive computing. In Proc. of the 2002 Mext-NSF-JSPS Int. Conf. on Software Security: Theories and Syst. (ISSS’02). 1--15.
[12]
A. Capone, M. Barros, H. Hrasnica, and S. Tompros. 2009. A New Architecture for Reduction of Energy Consumption of Home Appliances. In Proc. of the Europ. Conf. of the Czech Presidency of the Council of the EU “Towards eEnvironment.” 1--8.
[13]
Y. H. Chen, C. H. Lu, K. C. Hsu, L. C. Fu, Y. J. Yeh, and L. C. Kuo. 2009. Preference model assisted activity recognition learning in a smart home environment. In Proc. of the 2009 IEEE/RSJ Int. Conf. on Intelligent Robots and Syst. (IROS’09). 4657--4662.
[14]
Z. Y. Chen, C. L. Wu, and L. C. Fu. 2006. Using semi-supervised learning to build bayesian network for personal preference modeling in home environment. In Proc. of the 2006 IEEE Int. Conf. on Syst., Man, Cybern. (SMC’06), vol. 5. 3816--3821.
[15]
J. Choi, D. Shin, and D. Shin. 2005. Research and implementation of the context-aware middleware for controlling home appliances. IEEE Trans. on Consumer Electronics 51, 1 (2005), 301--306.
[16]
D. J. Cook. 2010. Learning setting-generalized activity models for smart spaces. IEEE Intell. Syst. 27, 1 (2010), 32--38.
[17]
D. J. Cook and S. K. Das. 2004. Smart Environments: Technology, Protocols and Applications.
[18]
D. J. Cook and S. K. Das. 2007. How smart are our environments? An updated look at the state of the art. Pervasive and Mobile Computing 3, 2 (2007), 53--73.
[19]
D. J. Cook and M. Schmitter-Edgecombe. 2009. Assessing the quality of activities in a smart environment. Methods of Informaion in Medicine 48, 5 (2009), 480--485.
[20]
F. Corucci, G. Anastasi, and F. Marcelloni. 2011. A WSN-based testbed for energy efficiency in buildings. In Proc. of the 16th IEEE Symp. on Computers and Commun. (ISCC’11). 990--993.
[21]
D. B. Crawley, L. K. Lawrie, F. C. Winkelmann, W. F. Buhl, Y. J. Huang, C. O. Pedersen, R. K. Strand, R. J. Liesen, D. E. Fisher, M. J. Witte, and others. 2001. EnergyPlus: Creating a new-generation building energy simulation program. Energy and Buildings 33, 4 (2001), 319--331.
[22]
S. Darby. 2006. The effectiveness of feedback on energy consumption. Technical Report, Environmental Change Institute, University of Oxford. Available at http://www.eci.ox.ac.uk/research/energy/downloads/smart-metering-report.pdf.
[23]
S. K. Das, D. J. Cook, A. Battacharya, E. O. III Heierman, and T. Y. Lin. 2002. The role of prediction algorithms in the MavHome smart home architecture. IEEE Wireless Commun. 9, 6 (2002), 77--84.
[24]
S. Dawson-Haggerty, X. Jiang, G. Tolle, J. Ortiz, and D. Culler. 2010. sMAP: A simple measurement and actuation profile for physical information. In Proc. of the 8th ACM Conf. on Embedded Networked Sensor Syst. 197--210.
[25]
S. Dawson-Haggerty, A. Krioukov, J. Taneja, S. Karandikar, G. Fierro, N. Kitaev, and D. Culler. 2013. BOSS: Building operating system services. In Proc. of the 10th USENIX Symp. on Networked Syst. Design and Implementation (NSDI’13). 443--458.
[26]
S. Dawson-Haggerty, J. Ortiz, X. Jiang, J. Hsu, S. Shankar, and D. Culler. 2010. Enabling green building applications. In Proc. of the 6th Workshop on Hot Topics in Embedded Networked Sensors. 1--5.
[27]
A. De Paola, S. Gaglio, G. Lo Re, and M. Ortolani. 2011. Multi-sensor fusion through adaptive Bayesian networks. In AI*IA 2011: Artificial Intelligence Around Man and Beyond. Lecture Notes in Computer Science, Vol. 6934. 360--371.
[28]
A. De Paola, S. Gaglio, G. Lo Re, and M. Ortolani. 2012. Sensor9k: A testbed for designing and experimenting with WSN-based ambient intelligence applications. Pervasive and Mobile Computing 8, 3 (2012), 448--466.
[29]
F. Doctor, H. Hagras, and V. Callaghan. 2005. A type-2 fuzzy embedded agent to realise ambient intelligence in ubiquitous computing environments. Information Sciences 171, 4 (2005), 309--334.
[30]
R. H. Dodier, G. P. Henze, D. K. Tiller, and X. Guo. 2006. Building occupancy detection through sensor belief networks. Energy and Buildings 38, 9 (2006), 1033--1043.
[31]
P. Ducange, F. Marcelloni, and D. Marinari. 2012. An algorithm based on finite state machines with fuzzy transitions for non-intrusive load disaggregation. In Proc. of the IFIP/IEEE Int. Conf. on Sustainable Internet and ICT for Sustainability. 1--6.
[32]
T. V. Duong, H. H. Bui, D. Q. Phung, and S. Venkatesh. 2005. Activity recognition and abnormality detection with the switching hidden semi-markov model. In Proc. of the 2005 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR’05), Vol. 1. 838--845.
[33]
P. G. Ellis, B. Griffith, N. Long, P. Torcellini, and D. Crawley. 2006. Automated multivariate optimization tool for energy analysis. In Proc. of the 2006 IBPSA-USA Conf. (SimBuild’06). 42--48.
[34]
P. G. Ellis and P. A. Torcellini. 2005. Simulating tall buildings using EnergyPlus. In Proc. of the 9th Int. IBPSA Conf. 279--286.
[35]
F. Englert, T. Schmitt, S. Kößler, A. Reinhardt, and R. Steinmetz. 2013. How to auto-configure your smart home?: High-resolution power measurements to the rescue. In Proc. of the 4th Int. Conf. on Future energy Syst. (e-Energy’13). 215--224.
[36]
V. L. Erickson, M. A. Carreira-Perpinan, and A. E. Cerpa. 2011. OBSERVE: Occupancy-based system for efficient reduction of HVAC energy. In Proc. of the 10th ACM/IEEE Int. Conf. on Information Processing in Sensor Networks (IPSN’11). 258--269.
[37]
A. Fernández-Montes, L. Gonzalez-Abril, J. A. Ortega, and F. V. Morente. 2009. A study on saving energy in artificial lighting by making smart use of wireless sensor networks and actuators. IEEE Network 23, 6 (2009), 16--20.
[38]
R. T. Fielding and R. N. Taylor. 2002. Principled design of the modern Web architecture. ACM Trans. Internet Technol. 2, 2 (2002), 115--150.
[39]
A. Foglar and S. Plosz. 2008. Appliance Profiles Specification. AIM Consortium, Deliverable 2.3. Available at http://www.ict-aim.eu/fileadmin/user_files/deliverables/AIM-D2-3v1-0.pdf.
[40]
J. Froehlich, E. Larson, S. Gupta, G. Cohn, M. Reynolds, and S. Patel. 2011. Disaggregated end-use energy sensing for the smart grid. IEEE Pervasive Computing 10, 1 (2011), 28--39.
[41]
G. Gao and K. Whitehouse. 2009. The self-programming thermostat: Optimizing setback schedules based on home occupancy patterns. In Proc. of the 1st ACM Work. on Embedded Sensing Syst. for Energy-Efficiency in Buildings. 67--72.
[42]
G. Ghidini and S. K. Das. 2012. Improving home energy efficiency with E2Home: A Web-based application for integrated electricity consumption and contextual information visualization. In Proc. of the IEEE 3rd Int. Conf. on Smart Grid Commun. (SmartGridComm). 471--475.
[43]
C. Gomez and J. Paradells. 2010. Wireless home automation networks: A survey of architectures and technologies. IEEE Commun. Magazine 48, 6 (2010), 92--101.
[44]
T. Gu, H. K. Pung, and D. Q. Zhang. 2004. Toward an OSGi-based infrastructure for context-aware applications. IEEE Pervasive Computing 3, 4 (2004), 66--74.
[45]
D. Guinard, V. Trifa, F. Mattern, and E. Wilde. 2011. From the internet of things to the web of things: Resource-oriented architecture and best practices. In Architecting the Internet of Things. 97--129.
[46]
S. Gupta, M. S. Reynolds, and S. N. Patel. 2010. ElectriSense: Single-point sensing using EMI for electrical event detection and classification in the home. In Proc. of the 12th ACM Int. Conf. on Ubiquitous computing. 139--148.
[47]
H. Hagras, F. Doctor, V. Callaghan, and A. Lopez. 2007. An incremental adaptive life long learning approach for type-2 fuzzy embedded agents in ambient intelligent environments. IEEE Trans. on Fuzzy Syst. 15, 1 (2007), 41--55.
[48]
F. Hammad and B. Abu-Hijleh. 2010. The energy savings potential of using dynamic external louvers in an office building. Energy and Buildings 42, 10 (2010), 1888--1895.
[49]
G. W. Hart. 1992. Nonintrusive appliance load monitoring. Proc. of the IEEE 80, 12 (1992), 1870--1891.
[50]
M. K. Hasan, K. A. P. Ngoc, Y. K. Lee, and S. Lee. 2009. Preference learning on an OSGi based home gateway. IEEE Trans. on Consumer Electronics 55, 3 (2009), 1322--1329.
[51]
S. Helal, W. Mann, H. El-Zabadani, J. King, Y. Kaddoura, and E. Jansen. 2005. The gator tech smart house: A programmable pervasive space. Computer 38, 3 (2005), 50--60.
[52]
A. Holmes, H. Duman, and A. Pounds-Cornish. 2002. The iDorm: Gateway to heterogeneous networking environments. In Proc. of the Int. ITEA Work. on Virtual Home Environments. 1--8.
[53]
Integrated environmental Solutions - Virtual Environments (IES-VE). 2013. Homepage. Available at http://www.iesve.com/.
[54]
International Energy Agency. 2003. Cool Appliance - Policy Strategies for Energy Efficient Homes. IEA Publications, Paris, France. Available at 62.168.68.98/StandardsLabels/downloads/01.pdf
[55]
X. Jiang, S. Dawson-Haggerty, P. Dutta, and D. Culler. 2009a. Design and implementation of a high-fidelity ac metering network. In Proc. of the 2009 Int. Conf. on information Processing in Sensor Networks. 253--264.
[56]
X. Jiang, M. Van Ly, J. Taneja, P. Dutta, and D. Culler. 2009b. Experiences with a high-fidelity wireless building energy auditing network. In Proc. of the 7th ACM Conf. on Embedded Networked Sensor Syst. 113--126.
[57]
A. Kamilaris, V. Trifa, and A. Pitsillides. 2011. HomeWeb: An application framework for Web-based smart homes. In Proc. of the 18th Int. Conf. on Telecommunications (ICT’11). 134--139.
[58]
W. Kastner, M. J. Kofler, and C. Reinisch. 2010. Using AI to realize energy efficient yet comfortable smart homes. In Proc. of the 2010 8th IEEE International Work. on Factory Communication Syst. (WFCS’10). 169--172.
[59]
A. H. Khalili, C. Wu, and H. Aghajan. 2010. Hierarchical preference learning for light control from user feedback. In Proc. of the 2010 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition Work. (CVPRW’10). 56--62.
[60]
C. Kidd, R. Orr, G. Abowd, C. Atkeson, I. Essa, B. MacIntyre, E. Mynatt, T. Starner, and W. Newstetter. 1999. The aware home: A living laboratory for ubiquitous computing research. In Cooperative Buildings. Integrating Information, Organizations, and Architecture. Lecture Notes in Computer Science, Vol. 1670. 191--198.
[61]
J. A. Kientz, S. N. Patel, B. Jones, E. Price, E. D. Mynatt, and G. D. Abowd. 2008. The georgia tech aware home. In Proc. of the SIGCHI Conf. on Human Factors in Computing Syst. 3675--3680.
[62]
Y. Kim, T. Schmid, Z. M. Charbiwala, and M. B. Srivastava. 2009a. ViridiScope: Design and implementation of a fine grained power monitoring system for homes. In Proc. of the 11th Int. Conf. on Ubiquitous Computing. 245--254.
[63]
Y. Kim, T. Schmid, M. B. Srivastava, and Y. Wang. 2009b. Challenges in resource monitoring for residential spaces. In Proc. of the 1st ACM Work. on Embedded Sensing Syst. for Energy-Efficiency in Buildings. 1--6.
[64]
K. Kobayashi, M. Tsukahara, A. Tokumasu, K. Okuyama, K. Saitou, and Y. Nakauchi. 2011. Ambient intelligence for energy conservation. In Proc. of the 2011 IEEE/SICE Int. Symp. on System Integration (SII’11). 375--380.
[65]
K. Koile, K. Tollmar, D. Demirdjian, H. Shrobe, and T. Darrell. 2003. Activity zones for context-aware computing. In Proc. of the 5th Int. Conf. on Ubiquitous Computing (UbiComp’03). 90--106.
[66]
D. Kolokotsa, K. Niachou, V. Geros, K. Kalaitzakis, G. S. Stavrakakis, and M. Santamouris. 2005. Implementation of an integrated indoor environment and energy management system. Energy and Buildings 37, 1 (2005), 93--99.
[67]
N. Kushwaha, M. Kim, D. Y. Kim, and W. D. Cho. 2004. An intelligent agent for ubiquitous computing environments: smart home UT-AGENT. In Proc. of the 2nd IEEE Work. on Software Technologies for Future Embedded and Ubiquitous Syst. 157--159.
[68]
C. Laughman, K. Lee, R. Cox, S. Shaw, S. Leeb, L. Norford, and P. Armstrong. 2003. Power signature analysis. IEEE Power and Energy Magazine 1, 2 (2003), 56--63.
[69]
C. Lee, D. Nordstedt, and S. Helal. 2003. Enabling smart spaces with OSGi. IEEE Pervasive Computing 2, 3 (2003), 89--94.
[70]
K. Lee and J. E. Braun. 2006. Evaluation of methods for determining demand-limiting setpoint trajectories in commercial buildings using short-term data analysis. In Proc. of the 2006 IBPSA-USA Conf. (SimBuild’06). 107--114.
[71]
J. Lifton, M. Feldmeier, Y. Ono, C. Lewis, and J. A. Paradiso. 2007. A platform for ubiquitous sensor deployment in occupational and domestic environments. In Proc. of the 6th Int. Conf. on Information Processing in Sensor Networks. 119--127.
[72]
Z. H. Lin and L. C. Fu. 2007. Multi-user preference model and service provision in a smart home environment. In Proc. of the 2007 IEEE Int. Conf. on Automation Science and Engineering (CASE’07). 759--764.
[73]
C. H. Lu and L. C. Fu. 2009. Robust location-aware activity recognition using wireless sensor network in an attentive home. IEEE Trans. on Automation Science and Engineering 6, 4 (2009), 598--609.
[74]
J. Lu, D. Birru, and K. Whitehouse. 2010a. Using simple light sensors to achieve smart daylight harvesting. In Proc. of the 2nd ACM Work. on Embedded Sensing Syst. for Energy-Efficiency in Building. 73--78.
[75]
J. Lu, T. Sookoor, V. Srinivasan, G. Gao, B. Holben, J. Stankovic, E. Field, and K. Whitehouse. 2010b. The smart thermostat: Using occupancy sensors to save energy in homes. In Proc. of the 8th ACM Conf. on Embedded Networked Sensor Syst. (SenSys’10). 211--224.
[76]
M. L. Marceau and R. Zmeureanu. 2000. Nonintrusive load disaggregation computer program to estimate the energy consumption of major end uses in residential buildings. Energy Conversion and Management 41, 13 (2000), 1389--1403.
[77]
A. Marchiori, D. Hakkarinen, Q. Han, and L. Earle. 2011. Circuit-level load monitoring for household energy management. IEEE Pervasive Computing 10, 1 (2011), 40--48.
[78]
Microsoft Hohm. 2011. Homepage. Available at http://www.microsoft.com/environment/.
[79]
M. Milenkovic and O. Amft. 2013. An opportunistic activity-sensing approach to save energy in office buildings. In Proc. of the 4th Int. Conf. on Future Energy Syst. 247--258.
[80]
L. Mottola and G. P. Picco. 2011. Programming wireless sensor networks: Fundamental concepts and state of the art. ACM Comput. Surv. 43, 3 (2011), 19:1--19:51.
[81]
M. C. Mozer. 1998. The neural network house: An environment hat adapts to its inhabitants. In Proc. of the Intelligent Environments AAAI Spring Symp. 110--114.
[82]
E. F. Nakamura, A. A. F. Loureiro, and A. C. Frery. 2007. Information fusion for wireless sensor networks: Methods, models, and classifications. ACM Comput. Surveys 39, 3 (2007), 9:1--9:55.
[83]
N. T. Nguyen, D. Q. Phung, S. Venkatesh, and H. Bui. 2005. Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model. In Proc. of the 2005 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR’05), Vol. 2. 955--960.
[84]
B. W. Olesen and K. C. Parsons. 2002. Introduction to thermal comfort standards and to the proposed new version of EN ISO 7730. Energy and Buildings 34, 6 (2002), 537--548.
[85]
M. S. Pan, L. W. Yeh, Y. A. Chen, Y. H. Lin, and Y. C. Tseng. 2008. A WSN-based intelligent light control system considering user activities and profiles. IEEE Sensors Journal 8, 10 (2008), 1710--1721.
[86]
S. N. Patel, T. Robertson, J. A. Kientz, M. S. Reynolds, and G. D. Abowd. 2007. At the flick of a switch: Detecting and classifying unique electrical events on the residential power line. In Proc. of the 9th Int. Conf. on Ubiquitous Computing. 271--288.
[87]
L. Perez-Lombard, J. Ortiz, and C. Pout. 2008. A review on buildings energy consumption information. Energy and Buildings 40, 3 (2008), 394--398.
[88]
J. Ü. Pfafferott, S. Herkel, D. E. Kalz, and A. Zeuschner. 2007. Comparison of low-energy office buildings in summer using different thermal comfort criteria. Energy and Buildings 39, 7 (2007), 750--757.
[89]
M. Philipose, K. P. Fishkin, M. Perkowitz, D. J. Patterson, D. Fox, H. Kautz, and D. Hahnel. 2004. Inferring activities from interactions with objects. IEEE Pervasive Computing 3, 4 (2004), 50--57.
[90]
Google PowerMeter. 2011. Homepage. Available at http://www.google.com/powermeter.
[91]
A. Prudenzi. 2002. A neuron nets based procedure for identifying domestic appliances pattern-of-use from energy recordings at meter panel. In Proc. of the 2002 IEEE Power Engineering Society Winter Meeting, Vol. 2. 941--946.
[92]
Pulse Energy Inc. 2013. Berkeley Energy Dashboard. Available at https://us.pulseenergy.com/UniCalBerkeley/dashboard.
[93]
Z. Qiu and G. Deconinck. 2011. Smart Meter’s feedback and the potential for energy savings in household sector: A survey. In Proc. of the 2011 IEEE Int. Conf. on Networking, Sensing and Control. 281--286.
[94]
P. Rashidi, D. J. Cook, L. Holder, and M. Schmitter-Edgecombe. 2011. Discovering activities to recognize and track in a smart environment. IEEE Trans. on Knowledge and Data Engineering 23, 4 (2011), 527--539.
[95]
P. Remagnino and G. L. Foresti. 2005. Ambient intelligence: A new multidisciplinary paradigm. IEEE Trans. on Syst. Man Cybern. A Syst. Humans 35, 1 (2005), 1--6.
[96]
L. Richardson and S. Ruby. 2007. RESTful Web Services.
[97]
A. Roy, S. K. Das, and K. Basu. 2007. A predictive framework for location-aware resource management in smart homes. IEEE Trans. on Mobile Computing 6, 11 (2007), 1270--1283.
[98]
A. G. Ruzzelli, C. Nicolas, A. Schoofs, and G. M. P. O’Hare. 2010. Real-time recognition and profiling of appliances through a single electricity sensor. In Proc. of 2010 7th Annual IEEE Commun. Society Conf. on Sensor Mesh and Ad Hoc Commun. and Networks (SECON’10). 1--9.
[99]
F. Sadri. 2011. Ambient intelligence: A survey. ACM Comput. Surveys 43, 4 (2011), 36:1--36:66.
[100]
A. Schoofs, A. G. Ruzzelli, and G. M. P. O’Hare. 2010. Appliance activity monitoring using wireless sensors. In Proc. of the 9th ACM/IEEE Int. Conf. on Information Processing in Sensor Networks. 434--435.
[101]
A. Schumann, M. Burillo, and N. Wilson. 2010. Predicting the desired thermal comfort conditions for shared offices. In Proc. of the Int. Conf. on Computing in Civil and Building Engineering (ICCCBE’10). 95--96.
[102]
V. Singhvi, A. Krause, C. Guestrin, J. H. Garrett Jr, and H. S. Matthews. 2005. Intelligent light control using sensor networks. In Proc. of the 3rd Int. Conf. on Embedded Networked Sensor Syst. 218--229.
[103]
R. S. Sutton and A. G. Barto. 1998. Reinforcement Learning: An Introduction.
[104]
S. Taherian, M. Pias, G. Coulouris, and J. Crowcroft. 2010. Profiling energy use in households and office spaces. In Proc. of the 1st Int. Conf. on Energy-Efficient Computing and Networking. 21--30.
[105]
E. Tapia, S. Intille, and K. Larson. 2004. Activity recognition in the home using simple and ubiquitous sensors. In Pervasive Computing. Lecture Notes in Computer Science, Vol. 3001. 158--175.
[106]
L. V. Thanayankizil, S. K. Ghai, D. Chakraborty, and D. P. Seetharam. 2012. Softgreen: Towards energy management of green office buildings with soft sensors. In Proc. of the 2012 4th Int. Conf. on Communication Systems and Networks (COMSNETS’12). 1--6.
[107]
The AIM Consortium. 2008. AIM—A Novel Architecture for Modelling, Virtualising and Managing the Energy Consumption of Household Appliances. Available at http://www.ict-aim.eu/.
[108]
The ESTIA Consortium. 2008. Enhanced Networked Architecture for Personalised Provision of AV Content within the Home Environment. Available at http://www.gorenjegroup.com/en/filelib/gorenje_group/eu_projects/eu_project_estia.pdf.
[109]
S. Tompros, N. Mouratidis, M. Caragiozidis, H. Hrasnica, and A. Gavras. 2008. A pervasive network architecture featuring intelligent energy management of households. In Proc. of the 1st Int. Conf. on PErvasive Technologies Related to Assistive Environments. 1--6.
[110]
S. Tompros, N. Mouratidis, M. Draaijer, A. Foglar, and H. Hrasnica. 2009. Enabling applicability of energy saving applications on the appliances of the home environment. IEEE Network 23, 6 (2009), 8--15.
[111]
U.S. Energy Information Administration. 2010. International Energy Outlook 2010—Highlights. Report DOE/EIA-0484(2010). Available at http://www.eia.doe.gov/oiaf/ieo/highlights.html.
[112]
U.S. Environmental Protection Agency (EPA). 2013. EnergyStar Program. Available at http://www.energystar.gov.
[113]
A. M. Vainio, M. Valtonen, and J. Vanhala. 2008. Proactive fuzzy control and adaptation methods for smart homes. IEEE Intell. Syst. 23, 2 (2008), 42--49.
[114]
T. van Kasteren, G. Englebienne, and B. Kröse. 2011. Hierarchical activity recognition using automatically clustered actions. In Ambient Intelligence. Lecture Notes in Computer Science, Vol. 7040. 82--91.
[115]
F. I. Vazquez and W. Kastner. 2011. Clustering methods for occupancy prediction in smart home control. In Proc. of the 2011 IEEE Int. Symp. on Industrial Electronics (ISIE’11). 1321--1328.
[116]
M. Weiser. 1991. The computer for the 21st century. Scientific American 265, 3 (1991), 66--75.
[117]
M. Weiss and D. Guinard. 2010. Increasing energy awareness through web-enabled power outlets. In Proc. of the 9th Int. Conf. on Mobile and Ubiquitous Multimedia. 20:1--20:10.
[118]
Y. J. Wen and A. M. Agogino. 2008. Wireless networked lighting systems for optimizing energy savings and user satisfaction. In Proc. of the 2008. IEEE Wireless Hive Networks Conf. (WHNC’08). 1--7.
[119]
D. Wilson and C. Atkeson. 2005. Simultaneous tracking and activity recognition (STAR) using many anonymous, binary sensors. In Pervasive Computing. Lecture Notes in Computer Science, Vol. 3468. 329--334.
[120]
WiSensys. 2011. Homepage. Available at http://www.wisensys.com.
[121]
X10. 2013. Homepage. Available at http://www.x10.com.
[122]
H. W. Yeh, C. H. Lu, Y. C. Huang, T. H. Yang, and L. C. Fu. 2011. Cloud-enabled adaptive activity-aware energy-saving system in a dynamic environment. In Proc. of the 2011 IEEE 9th Int. Conf. on Dependable, Autonomic and Secure Computing (DASC’11). 690--696.
[123]
L. W. Yeh, C. Y. Lu, C. W. Kou, Y. C. Tseng, and C. W. Yi. 2010. Autonomous light control by wireless sensor and actuator networks. IEEE Sensors J. 10, 6 (2010), 1029--1041.
[124]
L. W. Yeh, Y. C. Wang, and Y. C. Tseng. 2009. iPower: An energy conservation system for intelligent buildings by wireless sensor networks. Int. J. of Sensor Networks 5, 1 (2009), 1--10.

Cited By

View all
  • (2025) Enhanced Electricity Forecasting for Smart Buildings Using a TCN ‐Bi‐ LSTM Deep Learning Model Expert Systems10.1111/exsy.7000042:3Online publication date: 30-Jan-2025
  • (2025)Research on automated optimization of low-carbon architectural landscape spaces based on computer vision and machine learningInternational Journal of Low-Carbon Technologies10.1093/ijlct/ctae28020(146-153)Online publication date: 24-Jan-2025
  • (2025)Step-by-step time discrete Physics-Informed Neural Networks with application to a sustainability PDE modelMathematics and Computers in Simulation10.1016/j.matcom.2024.10.043230(541-558)Online publication date: Apr-2025
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Computing Surveys
ACM Computing Surveys  Volume 47, Issue 1
July 2014
551 pages
ISSN:0360-0300
EISSN:1557-7341
DOI:10.1145/2620784
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 June 2014
Accepted: 01 April 2014
Revised: 01 December 2013
Received: 01 September 2013
Published in CSUR Volume 47, Issue 1

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Building management systems
  2. ambient intelligence
  3. energy saving

Qualifiers

  • Research-article
  • Research
  • Refereed

Funding Sources

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)178
  • Downloads (Last 6 weeks)17
Reflects downloads up to 08 Mar 2025

Other Metrics

Citations

Cited By

View all
  • (2025) Enhanced Electricity Forecasting for Smart Buildings Using a TCN ‐Bi‐ LSTM Deep Learning Model Expert Systems10.1111/exsy.7000042:3Online publication date: 30-Jan-2025
  • (2025)Research on automated optimization of low-carbon architectural landscape spaces based on computer vision and machine learningInternational Journal of Low-Carbon Technologies10.1093/ijlct/ctae28020(146-153)Online publication date: 24-Jan-2025
  • (2025)Step-by-step time discrete Physics-Informed Neural Networks with application to a sustainability PDE modelMathematics and Computers in Simulation10.1016/j.matcom.2024.10.043230(541-558)Online publication date: Apr-2025
  • (2024)The Effect of Architectural Standards on Energy Consumption in High-Rise Residential Building in Northern IraqSustainability10.3390/su16241103216:24(11032)Online publication date: 16-Dec-2024
  • (2023)The Intersection Between Artificial Intelligence and Environmental SustainabilityExploring Ethical Dimensions of Environmental Sustainability and Use of AI10.4018/979-8-3693-0892-9.ch002(28-53)Online publication date: 7-Dec-2023
  • (2023)A Comparative Analysis of Standard and Nano-Structured Glass for Enhancing Heat Transfer and Reducing Energy Consumption Using Metal and Oxide Nanoparticles: A ReviewSustainability10.3390/su1512922115:12(9221)Online publication date: 7-Jun-2023
  • (2023)Enhancing Energy Efficiency by Improving Internet of Things Devices Security in Intelligent Buildings via Niche Genetic Algorithm-Based Control TechnologyApplied Sciences10.3390/app13191071713:19(10717)Online publication date: 26-Sep-2023
  • (2023)Sensing within Smart Buildings: A SurveyACM Computing Surveys10.1145/359660055:13s(1-35)Online publication date: 13-Jul-2023
  • (2023)Enhancing the Efficiency of Energy Storage and Management Systems for Hybrid Renewable Energy Applications in Tall Apartment Buildings2023 International Conference for Technological Engineering and its Applications in Sustainable Development (ICTEASD)10.1109/ICTEASD57136.2023.10585257(159-164)Online publication date: 14-Nov-2023
  • (2023)Smart Spaces: Occupancy Detection using Adaptive Back-Propagation Neural Network2023 International Conference on Business Analytics for Technology and Security (ICBATS)10.1109/ICBATS57792.2023.10111286(1-6)Online publication date: 7-Mar-2023
  • Show More Cited By

View Options

Login options

Full Access

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media