Aqeel H. Kazmi
Gregory M. P. O'hare
Skip Abstract Section
Abstract
Reducing energy consumption within buildings has been an active area of research in the past decade; more recently, there has been an increased influx of activity, motivated by a variety of issues including legislative, tax-related, as well as an increased awareness of energy-related issues. Energy usage both in commercial and residential buildings represents a significant portion of overall energy consumption; however, much of this may be categorized as waste, that is, energy usage that does not fulfil a definite purpose. In the past decade, the viability of Wireless Sensor Network (WSN) technologies has been demonstrated, leading to increased possibilities for novel services for building energy management. This development has resulted in numerous approaches being proposed for harnessing WSNs for energy management and conservation. This article surveys the state-of-the-art in building energy management systems. A generic architecture is proposed after which a detailed taxonomy of existing documented systems is presented. Gaps in the literature are highlighted and directions for future research identified.
- W. Abrahamse, L. Steg, C. Vlek, and T. Rothengatter. 2005. A review of intervention studies aimed at household energy conservation. J. Environ. Psychol. 25, 3, 273--291.Google Scholar
Cross Ref
- A. Adrian, R. Ram, and S. Raffi. 2011. Power demand distributions: Segmenting consumers using smart meter data. In Proceedings of the 3rd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building (BuildSys'11). ACM Press, New York, 49--50. Google ScholarDigital Library
- Y. Agarwal, B. Balaji, S. Dutta, R. Gupta, and T. Weng. 2011. Duty-cycling buildings aggressively: The next frontier in hvac control. In Proceedings of the 10th International Conference on Information Processing in Sensor Networks (IPSN'11). 246--257.Google Scholar
- Y. Agarwal, B. Balaji, R. Gupta, J. Lyles, M. Wei, and T. Weng. 2010. Occupancy-driven energy management for smart building automation. In Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building (BuildSys'10). ACM Press, New York, 1--6. Google ScholarDigital Library
- Y. Agarwal, R. Gupta, D. Komaki, and T. Weng. 2012. Buildingdepot: An extensible and distributed architecture for building data storage, access and sharing. In Proceedings of the 4th ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'12). ACM Press, New York, 64--71. Google ScholarDigital Library
- Y. Agarwal, T. Weng, and R. K. Gupta. 2009. The energy dashboard: Improving the visibility of energy consumption at a campus-wide scale. In Proceedings of the 1st ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'09). ACM Press, New York, 55--60. Google ScholarDigital Library
- M. Alahmad, P. Wheeler, A. Schwer, J. Eiden, and A. Brumbaugh. 2012. A comparative study of three feedback devices for residential real-time energy monitoring. IEEE Trans. Industr. Electron. 59, 4, 2002--2013.Google ScholarCross Ref
- H. N. Alberto and A. S. F. Flavio. 2007. Use of simulation tools for managing buildings energy demand. http://www.ibpsa.org/proceedings/BS2007/p494_final.pdf.Google Scholar
- W. Anderson and V. White. 2009. Exploring consumer preferences for home energy display functionality. Energy saving trust. Centre for Sustainable Energy, Bristol. http://www.cse.org.uk/pdf/consumer_preferences_for_home_energy_display.pdfGoogle Scholar
- K. Andrew, D.-H. Stephen, L. Linda, R. Omar, and C. David. 2011. A living laboratory study in personalized automated lighting controls. In Proceedings of the 3rd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building (BuildSys'11). ACM Press, New York, 1--6. Google ScholarDigital Library
- S. Anthony, D. Declan, and R. Antonio. 2011. Copolan: Non-invasive occupancy profiling for preliminary assessment of hvac fixed timing strategies. In Proceedings of the 3rd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building (BuildSys'11). ACM Press, New York, 53--54. Google ScholarDigital Library
- P. Arjunan, N. Batra, H. Choi, A. Singh, P. Singh, and M. B. Srivastava. 2012. Sensoract: A privacy and security aware federated middleware for building management. In Proceedings of the 4th ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'12). ACM Press, New York, 80--87. Google ScholarDigital Library
- S. Z. Attari, M. L. Dekay, C. I. Davidson, and W. Bruine De Bruin. 2010. Public perceptions of energy consumption and savings. http://www.pnas.org/content/early/2010/08/06/1001509107.abstract.Google Scholar
- M. Baranski and J. Voss. 2004. Genetic algorithm for pattern detection in nialm systems. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. Vol. 4, 3462--3468.Google Scholar
- A. Barbato, L. Borsani, A. Capone, and S. Melzi. 2009. Home energy saving through a user profiling system based on wireless sensors. In Proceedings of the 1st ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'09). ACM Press, New York, 49--54. Google ScholarDigital Library
- G. Barrenetxea, F. Ingelrest, G. Schaefer, and M. Vetterli. 2008. The hitchhiker's guide to successful wireless sensor network deployments. In Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems (SenSys'08). ACM Press, New York, 43--56. Google ScholarDigital Library
- O. Bates, A. K. Clear, A. Friday, M. Hazas, and J. Morley. 2012. Accounting for energy-reliant services within everyday life at home. In Proceedings of the 10th International Conference on Pervasive Computing. Springer, 107--124. Google ScholarDigital Library
- G. Bauer, K. Stockinger, and P. Lukowicz. 2009. Recognizing the use-mode of kitchen appliances from their current consumption. In Smart Sensing and Context, P. Barnaghi, K. Moessner, M. Presser, and S. Meissner, Eds., Lecture Notes in Computer Science, vol. 5741, Springer, 163--176. Google ScholarDigital Library
- C. Baumann, S. Holzer, M. Rodriguez, and R. Wattenhofer. 2012. Smart energy case study. In Proceedings of the 4th ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'12). ACM Press, New York, 36--38. Google ScholarDigital Library
- BDA. 2011. Building design advisor (lawrence berkeley national lab). http://gaia.lbl.gov/BDA.Google Scholar
- C. Beckmann, S. Consolvo, and A. Lamarca. 2004. Some assembly required: Supporting end-user sensor installation in domestic ubiquitous computing environments. In Ubiquitous Computing, N. Davies, E. Mynatt, and I. Siio, Eds., Lecture Notes in Computer Science, vol. 3205, Springer, 107--124.Google Scholar
- D. Berdichevsky and E. Neuenschwander. 1999. Toward an ethics of persuasive technology. Comm. ACM 42, 5, 51--58. Google ScholarDigital Library
- M. Berges, E. Goldman, H. S. Matthews, L. Soibelman, and K. Anderson. 2011. User-centered non-intrusive electricity load monitoring for residential buildings. ASCE J. Comput. Civil Engin. 25, 471--480.Google ScholarCross Ref
- M. E. Berges, E. Goldman, H. S. Matthews, and L. Soibelman. 2010. Enhancing electricity audits in residential buildings with nonintrusive load monitoring. J. Industr. Ecol. 14, 5, 844--858.Google ScholarCross Ref
- D. Bergman, D. Jin, J. Juen, N. Tanaka, C. Gunter, and A. Wright. 2011. Nonintrusive load-shed verification. IEEE Pervas. Comput. 10, 1, 49--57. Google ScholarDigital Library
- B. A. Bing Dong. 2009. Sensor-based occupancy behavioral pattern recognition for energy and comfort management in intelligent buildings. In Proceedings of the 11th International IBPSA Conference on Building Simulation.Google Scholar
- BLAST. 2013. Building loads analysis and system thermodynamics (blast). http://www.wbdg.org/tools.Google Scholar
- G. Brandon and A. Lewis. 1999. Reducing household energy consumption: A qualitative and quantitative field study. J. Environ. Psychol. 19, 1, 75--85.Google ScholarCross Ref
- J. Byun, I. Hong, and S. Park. 2012. Intelligent cloud home energy management system using household appliance priority based scheduling based on prediction of renewable energy capability. IEEE Trans. Consumer Electron. 58, 4, 1194--1201.Google ScholarCross Ref
- H.-H. Chang. 2012. Non-intrusive demand monitoring and load identification for energy management systems based on transient feature analyses. Energies 5, 11, 4569--4589.Google ScholarCross Ref
- H.-H. Chang, H.-T. Yang, and C.-L. Lin. 2008b. Load identification in neural networks for a nonintrusive monitoring of industrial electrical loads. In Computer Supported Cooperative Work in Design IV, W. Shen, J. Yong, Y. Yang, J.-P. Barths, and J. Luo, Eds., Lecture Notes in Computer Science, vol. 5236, Springer, 664--674. Google ScholarDigital Library
- J.-J. Chang, P.-C. Hsiu, and K. Tei-Wei. 2007. Search-oriented deployment strategies for wireless sensor networks. In Proceedings of the 10th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC'07). 164--171. Google ScholarDigital Library
- X. Chen, T. Wei, and S. Hu. 2013. Uncertainty-aware household appliance scheduling considering dynamic electricity pricing in smart home. IEEE Trans. Smart Grid 4, 2, 932--941.Google ScholarCross Ref
- Y. Cheng, K. Chen, B. Zhang, C.-J. M. Liang, X. Jiang, and F. Zhao. 2012. Accurate real-time occupant energy-footprinting in commercial buildings. In Proceedings of the 4th ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'12). ACM Press, New York, 115--122. Google ScholarDigital Library
- M. Chetty, D. Tran, and R. E. Grinter. 2008. Getting to green: Understanding resource consumption in the home. In Proceedings of the 10th International Conference on Ubiquitous Computing (UbiComp'08). ACM Press, New York, 242--251. Google ScholarDigital Library
- I. Cho, C.-C. Shen, S. Potbhare, S. Bhattacharyya, and N. Goldsman. 2011. Design methods for wireless sensor network building energy monitoring systems. In Proceedings of the 36th IEEE Conference on Local Computer Networks (LCN'11). 974--981. Google ScholarDigital Library
- E. K. Choe, S. Consolvo, J. Jung, B. L. Harrison, and J. A. Kientz. 2011. Living in a glass house: Survey of private moments in the home. In Proceedings of the 13th International Conference on Ubiquitous Computing (UbiComp'11). 41--44. Google ScholarDigital Library
- J. Christoffersen, E. Petersen, and K. Johnsen. 1997. An experimental evaluation of daylight systems and lighting control. In Proceedings of the Right Light 4th European Conference on Energy Efficient Lighting. Vol. 2, 245--254.Google Scholar
- J. Clarke, J. Cockroft, S. Conner, J. Hand, N. Kelly, R. Moore, T. Ołbrien, and P. Strachan. 2002. Simulation-assisted control in building energy management systems. Energy Buildings 34, 9, 933--940.Google ScholarCross Ref
- F. Corucci, G. Anastasi, and F. Marcelloni. 2011. A wsn-based testbed for energy efficiency in buildings. In Proceedings of the IEEE Symposium on Computers and Communications (ISCC'11). 990--993. Google ScholarDigital Library
- 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 J. Glazer. 2001. Energyplus: Creating a new-generation building energy simulation program. Energy Buildings 33, 4, 319--331.Google ScholarCross Ref
- D.-I. Curiac and C. Volosencu. 2010. Hierarchical lighting control in urban environments based on wireless sensor-actuator networks. In Proceedings of the 6th WSEAS International Conference on Dynamical Systems and Control (CONTROL'10). 163--166. Google ScholarDigital Library
- A. Cuzzocrea, G. Fortino, and O. Rana. 2013. Managing data and processes in cloud-enabled largescale sensor networks: State-of-the-art and future research directions. In Proceedings of the 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid'13). 583--588.Google Scholar
- G. A. Darbellay and M. Slama. 2000. Forecasting the short-term demand for electricity: Do neural networks stand a better chance? Int. J. Forecast. 16, 1, 71--83.Google ScholarCross Ref
- S. Darby. 2006. The effectiveness of feedback on energy consumption. A review for defra of the literature on metering, billing and direct displays. http://www.eci.ox.ac.uk/research/energy/downloads/smart-metering-report.pdf.Google Scholar
- S. Darby. 2010. Literature review for the energy demand research project. https://www.ofgem.gov.uk/ofgem-publications/59113/sd-ofgem-literature-review-final-081210.pdf.Google Scholar
- D. T. Delaney, G. M. P. Ohare, and A. G. Ruzzelli. 2009. Evaluation of energy-efficiency in lighting systems using sensor networks. In Proceedings of the 1st ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'09). ACM Press, New York, 61--66. Google ScholarDigital Library
- Distech Controllers. 2013. Distech's bacnet controllers. http://http://www.distech-controls.com.Google Scholar
- DOE-2. 2011. The home of doe-2 based building energy use and cost analysis software. http://www.doe2.com.Google Scholar
- A. R. Doherty, Z. Qiu, C. Foley, H. Lee, C. Gurrin, and A. F. Smeaton. 2010. Green multimedia: Informing people of their carbon footprint through two simple sensors. In Proceedings of the International Conference on Multimedia (MM'10). ACM Press, New York, 441--450. Google ScholarDigital Library
- H. Doukas, K. D. Patlitzianas, K. Iatropoulos, and J. Psarras. 2007. Intelligent building energy management system using rule sets. Building Environ. 42, 10, 3562--3569.Google ScholarCross Ref
- S. Drenker and A. Kader. 1999. Nonintrusive monitoring of electric loads. IEEE Comput. Appl. Power 12, 4, 47--51.Google ScholarCross Ref
- F. Drozd, T. Lehto, and H. Oinas-Kukkonen. 2012. Exploring perceived persuasiveness of a behavior change support system: A structural model. In Persuasive Technology. Design for Health and Safety, M. Bang and E. Ragnemalm, Eds., Lecture Notes in Computer Science, vol. 7284, Springer, 157--168. Google ScholarDigital Library
- A. Eastwell. 1990. The status of bems. In Proceedings of the IEE Colloquium on Control in Building Energy Management Systems. 6/1--6/3.Google Scholar
- Eia. 2013. US energy information administration. http://www.eia.gov.Google Scholar
- Energy-2020. 2011. European strategy, europe 2020 initiative - energy 2020. http://ec.europa.eu/energy/strategies/2010/Google Scholar
- Energy-Book. 2010. European commission statistical book on environment and energy. http://ec.europa.euGoogle Scholar
- Energyplus. 2011. Energyplus: Energy simulation software. http://apps1.eere.energy.gov/buildings/energyplus.Google Scholar
- V. L. Erickson and A. E. Cerpa. 2010. Occupancy based demand response hvac control strategy. In Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building (BuildSys'10). ACM Press, New York, 7--12. Google ScholarDigital Library
- V. L. Erickson and A. E. Cerpa. 2012. Thermovote: Participatory sensing for efficient building hvac conditioning. In Proceedings of the 4th ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'12). ACM Press, New York, 9--16. Google ScholarDigital Library
- V. L. Erickson, Y. Lin, A. Kamthe, R. Brahme, A. Surana, A. E. Cerpa, M. D. Sohn, and S. Narayanan. 2009. Energy efficient building environment control strategies using real-time occupancy measurements. In Proceedings of the 1st ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'09). ACM Press, New York, 19--24. Google ScholarDigital Library
- A. Faruqui, S. Sergici, and A. Sharif. 2010. The impact of informational feedback on energy consumption survey of the experimental evidence. Energy 35, 4, 1598--1608.Google ScholarCross Ref
- C. Fischer. 2008. Feedback on household electricity consumption: A tool for saving energy? Energy Efficiency 1, 79--104.Google Scholar
- G. Fitzpatrick and G. Smith. 2009. Technology-enabled feedback on domestic energy consumption: Articulating a set of design concerns. IEEE Pervas. Comput. 8, 1, 37--44. Google ScholarDigital Library
- J. Fogarty, C. Au, and S. E. Hudson. 2006. Sensing from the basement: A feasibility study of unobtrusive and low-cost home activity recognition. In Proceedings of the 19th Annual ACM Symposium on User Interface Software and Technology (UIST'06). ACM Press, New York, 91--100. Google ScholarDigital Library
- J. Froehlich, E. Larson, S. Gupta, G. Cohn, M. Reynolds, and S. Patel. 2011. Disaggregated end-use energy sensing for the smart grid. IEEE Pervas. Comput. 10, 1, 28--39. Google ScholarDigital Library
- G. Gao and K. Whitehouse. 2009. The self-programming thermostat: Optimizing setback schedules based on home occupancy patterns. In Proceedings of the 1st ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'09). ACM Press, New York, 67--72. Google ScholarDigital Library
- V. Garg and N. K. Bansal. 2000. Smart occupancy sensors to reduce energy consumption. Energy Buildings 32, 1, 81--87.Google ScholarCross Ref
- Google. 2011. Google powermeter for energy monitoring. http://www.google.com/powermeter.Google Scholar
- A. Guinard, A. McGibney, and D. Pesch. 2009a. A wireless sensor network design tool to support building energy management. In Proceedings of the 1st ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'09). ACM Press, New York, 25--30. Google ScholarDigital Library
- D. Guinard, M. Weiss, and V. Trifa. 2009b. Are you energy-efficient: Sense it on the web! https://www.webofthings.org/wp-content/uploads/2009/06/guinard_et_al_demo_camera_ready.pdf.Google Scholar
- 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 Proceedings of the 12th ACM International Conference on Ubiquitous Computing (Ubicomp'10). ACM Press, New York, 139--148. Google ScholarDigital Library
- T. Hargreaves, M. Nye, and J. Burgess. 2010. Making energy visible: A qualitative field study of how householders interact with feedback from smart energy monitors. Energy Policy 38, 10, 6111--6119.Google ScholarCross Ref
- G. W. Hart. 1992. Nonintrusive appliance load monitoring. Proc. IEEE 80, 12.Google ScholarCross Ref
- S. Hay and A. Rice. 2009. The case for apportionment. In Proceedings of the 1st ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'09). ACM Press, New York, 13--18. Google ScholarDigital Library
- B.-J. Ho, H.-L. C. Kao, N. Chen, C.-W. You, H.-H. Chu, and M.-S. Chen. 2011. Heatprobe: A thermal-based power meter for accounting disaggregated electricity usage. In Proceedings of the 13th International Conference on Ubiquitous Computing. 55--64. Google ScholarDigital Library
- T. G. Holmes. 2007. Eco-visualization: Combining art and technology to reduce energy consumption. In Proceedings of the 6th ACM SIGCHI Conference on Creativity and Cognition (C&C'07). ACM Press, New York, 153--162. Google ScholarDigital Library
- S. Houde, A. Todd, A. Sudarshan, J. Flora, and K. C. Armel. 2011. Real-time feedback and electricity consumption: A field experiment assessing the potential for savings and persistence. http://www.stanford.edu/group/peec/cgibin/docs/behavior/research/FieldExperimentPowermeter_vrevised_May2012_authors_vf_pdf.pdf.Google Scholar
- J. Hsu, P. Mohan, X. Jiang, J. Ortiz, S. Shankar, S. Dawson-Haggerty, and D. Culler. 2010. Hbci: Human-building-computer interaction. In Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building (BuildSys'10). ACM Press, New York, 55--60. Google ScholarDigital Library
- J. W. Hui and D. E. Culler. 2008. IP is dead, long live ip for wireless sensor networks. In Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems (SenSys'08). ACM Press, New York, 15--28. Google ScholarDigital Library
- R. B. Hutton, G. A. Mauser, P. Filiatrault, and O. T. Ahtola. 1986. Effects of cost-related feedback on consumer knowledge and consumption behavior: A field experimental approach. J. Consumer Res. 13, 3, 327--336.Google ScholarCross Ref
- ISO-13567. 2013. Organization and naming of layers for cad. http://www.iso.org.Google Scholar
- Y. Jaeyeong, P. Byungsung, and H. Kyeon. 2011. Context awareness-based disaggregation of residential load consumption. In Proceedings of the 18th International Federation of Automatic Control World Congress (IFAC'11).Google Scholar
- X. Jiang, S. Dawson-Haggerty, P. Dutta, and D. Culler. 2009a. Design and implementation of a high-fidelity ac metering network. In Proceedings of the International Conference on Information Processing in Sensor Networks (IPSN'09). IEEE Computer Society, 253--264. Google ScholarDigital Library
- X. Jiang, M. Van Ly, J. Taneja, P. Dutta, and D. Culler. 2009b. Experiences with a high-fidelity wireless building energy auditing network. In Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems (SenSys'09). ACM Press, New York, 113--126. Google ScholarDigital Library
- D. Jung and A. Savvides. 2010. Estimating building consumption breakdowns using on/off state sensing and incremental sub-meter deployment. In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems (SenSys'10). ACM Press, New York, 225--238. Google ScholarDigital Library
- G. Kalogridis, C. Efthymiou, S. Denic, T. Lewis, and R. Cepeda. 2010. Privacy for smart meters: Towards undetectable appliance load signatures. In Proceedings of the 1st IEEE International Conference on Smart Grid Communications (SmartGridComm'10). 232--237.Google Scholar
- A. Kamilaris, A. Pitsillides, and M. Yiallouros. 2013. Building energy-aware smart homes using web technologies. J. Ambient Intell. Smart Environ. 5, 2, 161--186. Google ScholarCross Ref
- J. D. Kaufman. 2001. Seegreen: A tool for real-time distributed monitoring of home electricity consumption. M.S. thesis, MIT Media Lab.Google Scholar
- M. A. Kazandjieva, B. Heller, P. Levis, and C. Kozyrakis. 2008. Spotlight: Personal natural resource consumption profiler. In Proceedings of the 5th Workshop on Embedded Networked Sensors (HotEmNets'08). http://nslab.ee.ntu.edu.tw/NetworkSeminar/papers/spot.pdf.Google Scholar
- Y. Kim, Z. M. Charbiwala, A. Singhania, T. Schmid, and M. B. Srivastava. 2009a. Energy dumpster diving. In Proceedings of the SOSP Workshop on Power Aware Computing and Systems (HotPower'09). http://csl.stanford.edu/∼christos/publications/2009.powernet.hotpower.pdf.Google Scholar
- Y. Kim, T. Schmid, Z. M. Charbiwala, and M. B. Srivastava. 2009b. Viridiscope: Design and implementation of a fine grained power monitoring system for homes. In Proceedings of the 11th International Conference on Ubiquitous Computing (Ubicomp'09). ACM Press, New York, 245--254. Google ScholarDigital Library
- Y. Kim, T. Schmid, M. B. Srivastava, and Y. Wang. 2009c. Challenges in resource monitoring for residential spaces. In Proceedings of the 1st ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'09). ACM Press, New York, 1--6. Google ScholarDigital Library
- M. Kovatsch, M. Weiss, and D. Guinard. 2010. Embedding Internet technology for home automation. In Proceedings of the IEEE Conference on Emerging Technologies and Factory Automation (ETFA'10). 1--8.Google Scholar
- A. Krioukov, G. Fierro, N. Kitaev, and D. Culler. 2012. Building application stack (bas). In Proceedings of the 4th ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'12). ACM Press, New York, 72--79. Google ScholarDigital Library
- W. Kurschl and W. Beer. 2009. Combining cloud computing and wireless sensor networks. In Proceedings of the 11th International Conference on Information Integration and Web-based Applications and Services (iiWAS'09). ACM Press, New York, 512--518. Google ScholarDigital Library
- H. Lam, G. Fung, and W. Lee. 2007. A novel method to construct taxonomy electrical appliances based on load signaturesof. IEEE Trans. Consumer Electron. 53, 2, 653--660. Google ScholarDigital Library
- T.-W. Lee, M. Lewicki, M. Girolami, and T. Sejnowski. 1999. Blind source separation of more sources than mixtures using overcomplete representations. IEEE Signal Process. Lett. 6, 4, 87--90.Google ScholarCross Ref
- S. Leeb and J. L. Kirtley. 1993. A multiscale transient event detector for nonintrusive load monitoring. In Proceedings of the International Conference on Industrial Electronics, Control, and Instrumentation (IECON'93), vol. 1. 354--359.Google Scholar
- 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 Proceedings of the 6th International Conference on Information Processing in Sensor Networks (IPSN'07). ACM Press, New York, 119--127. Google ScholarDigital Library
- X. Liu, B. Akinci, M. Berges, and J. H. Garrett JR. 2012. An integrated performance analysis framework for hvac systems using heterogeneous data models and building automation systems. In Proceedings of the 4th ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'12). ACM Press, New York, 145--152. Google ScholarDigital Library
- J. Lu, D. Birru, and K. Whitehouse. 2010a. Using simple light sensors to achieve smart daylight harvesting. In Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building (BuildSys'10). ACM Press, New York, 73--78. Google ScholarDigital Library
- 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 Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems (SenSys'10). ACM Press, New York, 211--224. Google ScholarDigital Library
- A. E.-D. Mady, G. Provan, and N. Wei. 2012. Designing cost-efficient wireless sensor/actuator networks for building control systems. In Proceedings of the 4th ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'12). ACM Press, New York, 138--144. Google ScholarDigital Library
- T. Maile, M. Fischer, and V. Bazjanac. 2007. Building energy performance simulation tools -A lifecycle and interoperable perspective. http://cife.stanford.edu/sites/default/files/WP107.pdf.Google Scholar
- A. Majumdar, D. H. Albonesi, and P. Bose. 2012. Energy-aware meeting scheduling algorithms for smart buildings. In Proceedings of the 4th ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'12). ACM Press, New York, 161--168. Google ScholarDigital Library
- J. Mankoff, D. Matthews, S. R. Fussell, and M. Johnson. 2007. Leveraging social networks to motivate individuals to reduce their ecological footprints. In Proceedings of the 40th Annual Hawaii International Conference on System Sciences (HICSS'07). 87--87. Google ScholarDigital Library
- A. Marchiori, D. Hakkarinen, Q. Han, and L. Earle. 2011. Circuit-level load monitoring for household energy management. IEEE Pervas. Comput. 10, 1, 40--48. Google ScholarDigital Library
- A. Marchiori and Q. Han. 2009. Using circuit-level power measurements in household energy management systems. In Proceedings of the 1st ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'09). ACM Press, New York, 7--12. Google ScholarDigital Library
- A. Marchiori and Q. Han. 2010. Distributed wireless control for building energy management? In Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building (BuildSys'10). ACM Press, New York, 37--42. Google ScholarDigital Library
- A. Marchiori, Q. Han, W. C. Navidi, and L. Earle. 2012. Building the case for automated building energy management. In Proceedings of the 4th ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'12). ACM Press, New York, 25--32. Google ScholarDigital Library
- D. Marsh, R. Tynan, D. Okane, and G. M. P. Ohare. 2004. Autonomic wireless sensor networks. Engin. Appl. Artif. Intell. 17, 7, 741--748. Google ScholarDigital Library
- D. Marshall. 1992. The past and future of building management systems. In Proceedings of the IEE Colloquium on Building Management Systems. 3--5.Google Scholar
- P. Martin, D. Angela, S. Hermann, K. Giorgos, G. Giorgos, K. Elias, and R. Dimitrios. 2011. Simulation-assisted building energy performance improvement using sensible control decisions. In Proceedings of the 3rd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building (BuildSys'11). ACM Press, New York, 61--66. Google ScholarDigital Library
- L. T. McCalley and C. J. H. Midden. 2002. Energy conservation through product-integrated feedback: The roles of goal-setting and social orientation. J. Econ. Psychol. 23, 5, 589--603.Google ScholarCross Ref
- L. McClelland and S. W. Cook. 1979. Energy conservation effects of continuous in-home feedback in all-electric homes. J. Environ. Syst. 9, 2, 169--173.Google ScholarCross Ref
- A. H. McMakin, E. L. Malone, and R. E. Lundgren. 2002. Motivating residents to conserve energy without financial incentives. Environ. Behav. 34, 848--863.Google ScholarCross Ref
- K. Menzel, D. Pesch, B. Oflynn, M. Keane, and C. Omathuna. 2008. Towards a wireless sensor platform for energy efficient building operation. Tsinghua Sci. Technol. Supplement 13, 1, 381--386.Google Scholar
- Microsoft. 2011. Microsoft hohm to monitor home energy consumption. http://www.microsoft-hohm.com.Google Scholar
- C. J. H. Midden, J. F. Meter, M. H. Weenig, and H. J. A. Zieverink. 1983. Using feedback, reinforcement and information to reduce energy consumption in households: A field-experiment. J. Econ. Psychol. 3, 1, 65--86.Google ScholarCross Ref
- A. Molina-Markham, P. Shenoy, K. Fu, E. Cecchet, and D. Irwin. 2010. Private memoirs of a smart meter. In Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building (BuildSys'10). ACM Press, New York, 61--66. Google ScholarDigital Library
- Nest. 2013. Nest learning thermostat. http://www.nest.com.Google Scholar
- A. H. Neto and F. A. S. Fiorelli. 2008. Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption. Energy Buildings 40, 12, 2169--2176.Google ScholarCross Ref
- N.-H. Nguyen, Q.-T. Tran, J.-M. Leger, and T.-P. Vuong. 2010. A real-time control using wireless sensor network for intelligent energy management system in buildings. In Proceedings of the IEEE Workshop on Environmental Energy and Structural Monitoring Systems (EESMS'10). 87--92.Google Scholar
- L. K. Norford and S. B. Leeb. 1996. Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms. Energy Buildings 24, 1, 51--64.Google ScholarCross Ref
- OGC-3D. 2013. OGC advances standards for the built environment and 3D. http://www.opengeospatial.org.Google Scholar
- G. M. P. O'Hare, C. Muldoon, M. J. Ogrady, R. W. Collier, O. Murdoch, and D. Carr. 2012. Sensor web interaction. Int. J. Artif. Intell. Tools 21, 2.Google ScholarCross Ref
- H. Oinas-Kukkonen. 2012. A foundation for the study of behavior change support systems. Person. Ubiq. Comput. 17, 6, 1223--1235. Google ScholarDigital Library
- OpenRoomMap. 2013. Openroommap: Annotate building floor maps with fine-grain positioned furniture, appliances and room features, in a user-friendly graphical user interface. http://www.cl.cam.ac.uk/research/dtg/openroommap.Google Scholar
- J. Padget, H. Riat, M. Warnier, F. Brazier, and S. Natarajan. 2010. An agent-based infrastructure for energy profile capture and management. In Proceedings of the 1st International Workshop on Agent Technologies for Energy Systems (ATES'10).Google Scholar
- K. Padmanabh, V. A. Malikarjuna, S. Sen, S. P. Katru, A. Kumar, C. S. Pawankumar, S. K. Vuppala, and S. Paul. 2009. Isense: A wireless sensor network based conference room management system. In Proceedings of the 1st ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'09). ACM Press, New York, 37--42. Google ScholarDigital Library
- M. Pallack, D. Cook, and J. Sullivan. 1980. Commitment and energy conservation. Appl. Social Psychol. 1, 1, 235--253.Google Scholar
- S. Park, H. Kim, H. Moon, J. Heo, and S. Yoon. 2010. Concurrent simulation platform for energy-aware smart metering systems. IEEE Trans. Consumer Electron. 56, 3, 1918--1926. Google ScholarDigital Library
- S. Patel, T. Robertson, J. Kientz, M. Reynolds, and G. Abowd. 2007. At the flick of a switch: Detecting and classifying unique electrical events on the residential power line. In Proceedings of the 9th International Conference on Ubiquitous Computing (UbiComp'07). J. Krumm, G. Abowd, A. Seneviratne, and T. Strang, Eds., Lecture Notes in Computer Science, vol. 4717, Springer, 271--288. Google ScholarDigital Library
- A. Pedrini, F. S. Westphal, and R. Lamberts. 2002. A methodology for building energy modelling and calibration in warm climates. Building Environ. 37, 8--9, 903--912.Google ScholarCross Ref
- D. Petersen, J. Steele, and J. Wilkerson. 2009. Wattbot: A residential electricity monitoring and feedback system. In Proceedings of the 27th International Conference Extended Abstracts on Human Factors in Computing Systems (CHI'09). ACM Press, New York, 2847--2852. Google ScholarDigital Library
- N. B. Priyantha, A. Kansal, M. Goraczko, and F. Zhao. 2008. Tiny web services: Design and implementation of interoperable and evolvable sensor networks. In Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems (SenSys'08). ACM Press, New York, 253--266. Google ScholarDigital Library
- A. Rice, S. Hay, and D. Ryder-Cook. 2010. A limited-data model of building energy consumption. In Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building (BuildSys'10). ACM Press, New York, 67--72. Google ScholarDigital Library
- D. Robins and J. Holmes. 2008. Aesthetics and credibility in web site design. Inf. Process. Manag. 44, 1, 386--399. Google ScholarDigital Library
- T. Rodden and S. Benford. 2003. The evolution of buildings and implications for the design of ubiquitous domestic environments. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI'03). ACM Press, New York, 9--16. Google ScholarDigital Library
- A. Rowe, M. Berges, and R. Rajkumar. 2010. Contactless sensing of appliance state transitions through variations in electromagnetic fields. In Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building (BuildSys'10). ACM Press, New York, 19--24. Google ScholarDigital Library
- A. Rowe, M. E. Berges, G. Bhatia, E. Goldman, R. Rajkumar, J. H. Garrett, J. M. F. Moura, and L. Soibelman. 2011. Sensor andrew: Large-scale campus-wide sensing and actuation. IBM J. Res. Devel. 55, 1.2, 6:1--6:14. Google ScholarDigital Library
- A. Ruzzelli, C. Nicolas, A. Schoofs, and G. O'Hare. 2010. Real-time recognition and profiling of appliances through a single electricity sensor. In Proceedings of the 7th Annual IEEE Communications Society Conference on Sensor Mesh and Ad Hoc Communications and Networks (SECON'10). 1--9.Google ScholarCross Ref
- J. S. Sandhu. 2004. Wireless sensor networks for commercial lighting control: Decision making with multiagent systems. In Proceedings of the AAAI Workshop on Sensor Networks. 131--140.Google Scholar
- T. Schmid, D. Culler, and P. Dutta. 2010. Meter any wire, anywhere by virtualizing the voltage channel. In Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building (BuildSys'10). ACM Press, New York, 25--30. Google ScholarDigital Library
- A. Schoofs, A. Guerrieri, D. T. Delaney, G. M. P. Ohare, and A. Ruzzelli. 2010a. Annot: Automated electricity data annotation using wireless sensor networks. In Proceedings of the 7th Annual IEEE Communications Society Conference on Sensor Mesh and Ad Hoc Communications and Networks (SECON'10), 2010. 1--9.Google Scholar
- A. Schoofs, A. G. Ruzzelli, and G. M. P. Ohare. 2010b. Appliance activity monitoring using wireless sensors. In Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN'10). ACM Press, New York, 434--435. Google ScholarDigital Library
- C. Seligman and J. M. Darley. 1976. Feedback as a means of decreasing residential energy consumption. J. Appl. Psychol. 62, 4, 363--368.Google ScholarCross Ref
- V. Singhvi, A. Krause, C. Guestrin, J. H. Garrett JR, and H. S. Matthews. 2005. Intelligent light control using sensor networks. In Proceedings of the 3rd International Conference on Embedded Networked Sensor Systems (SenSys'05). ACM Press, New York, 218--229. Google ScholarDigital Library
- A. Smeaton and A. Doherty. 2013. Persuading consumers to reduce their consumption of electricity in the home. In Persuasive Technology, S. Berkovsky and J. Freyne, Eds., Lecture Notes in Computer Science, vol. 7822, Springer, 204--215. Google ScholarDigital Library
- A. So and W. Chan. 2012. Intelligent Building Systems. International Series on Asian Studies in Computer and Information Science, Springer.Google Scholar
- J. Sousa, R. Babuska, P. Bruijn, and H. Verbruggen. 1996. Comparison of conventional and fuzzy predictive control. In Proceedings of the 5th IEEE International Conference on Fuzzy Systems. Vol. 3. 1782--1787.Google Scholar
- V. Srinivasan, J. Stankovic, and K. Whitehouse. 2008. Protecting your daily in-home activity information from a wireless snooping attack. In Proceedings of the 10th International Conference on Ubiquitous Computing (UbiComp'08). ACM Press, New York, 202--211. Google ScholarDigital Library
- Y. A. Strengers. 2011. Designing eco-feedback systems for everyday life. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI'11). ACM Press, New York, 2135--2144. Google ScholarDigital Library
- V. Sundramoorthy, G. Cooper, N. Linge, and Q. Liu. 2011. Domesticating energy-monitoring systems: Challenges and design concerns. IEEE Pervas. Comput. 10, 1, 20--27. Google ScholarDigital Library
- K. Suzuki, S. Inagaki, T. Suzuki, H. Nakamura, and K. Ito. 2008. Nonintrusive appliance load monitoring based on integer programming. In Proceedings of the SICE Annual Conference. 2742--2747.Google Scholar
- Z. C. Taysi, M. A. Guvensan, and T. Melodia. 2010. Tinyears: Spying on house appliances with audio sensor nodes. In Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building (BuildSys'10). ACM Press, New York, 31--36. Google ScholarDigital Library
- W. Thomas, B. Bharathan, D. Seemanta, G. Rajesh, and A. Yuvraj. 2011. Managing plug-loads for demand response within buildings. In Proceedings of the 3rd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building (BuildSys'11). ACM Press, New York, 13--18. Google ScholarDigital Library
- A. Trianni and E. Cagno. 2012. Dealing with barriers to energy efficiency and SMES: Some empirical evidences. Energy 37, 1, 494--504.Google ScholarCross Ref
- Trnsys. 2013. Transient system simulation tool. http://www.trnsys.com.Google Scholar
- R. Tynan, D. Marsh, D. Okane, and G. M. O'Hare. 2005. Intelligent agents for wireless sensor networks. In Proceedings of the 4th International Joint Conference on Autonomous Agents and Multiagent Systems. ACM Press, New York, 1179--1180. Google ScholarDigital Library
- U.S Department of Energy. 2011. Energy modeling and simulation software tools. http://www.eere.energy.gov.Google Scholar
- R. Vaishakh and R. Anthony. 2011. Low-cost continuous thermal sensing. In Proceedings of the 3rd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building (BuildSys'11). ACM Press, New York, 53--54. Google ScholarDigital Library
- G. Virk, K. Alkadhimi, J. Cheung, and D. Loveday. 1989. Advanced control techniques for bems. In Proceedings of the 1st International Congress on Condition Monitoring and Diagnostic Engineering Management (COMADEM'89). R. Rao and A. Hope, Eds., Springer, 463--468.Google Scholar
- J. Wall, G. Platt, G. James, and P. Valencia. 2007. Wireless sensor networks as agents for intelligent control of distributed energy resources. In Proceedings of the 2nd International Symposium on Wireless Pervasive Computing (ISWPC'07).Google Scholar
- S. Wang and J. Xie. 2002. Integrating building management system and facilities management on the internet. Autom. Construct. 11, 6, 707--715.Google ScholarCross Ref
- W. Wang and B. Hua. 2009. Application of the automatic control technology for energy conservation of the air-conditioning system in substations in shanghai. In Proceedings of the 20th International Conference and Exhibition on Electricity Distribution -- Part 1 (CIRED'09). 1--4.Google Scholar
- Y.-C. Wang, C.-C. Hu, and Y.-C. Tseng. 2005. Efficient deployment algorithms for ensuring coverage and connectivity of wireless sensor networks. In Proceedings of the 1st International Conference on Wireless Internet. IEEE Computer Society, 114--121. Google ScholarDigital Library
- H. Wasilowski and C. Reinhart. 2009. Modelling an existing building in designbuilder/e+: Custom versus default inputs. In Proceedings of the Building Simulation Conference.Google Scholar
- C. Wei and Y. Li. 2011. Design of energy consumption monitoring and energy-saving management system of intelligent building based on the internet of things. In Proceedings of the International Conference on Electronics, Communications and Control (ICECC'11). 3650--3652.Google Scholar
- M. Weiss, F. Mattern, T. Graml, T. Staake, and E. Fleisch. 2009. Handy feedback: Connecting smart meters with mobile phones. In Proceedings of the 8th International Conference on Mobile and Ubiquitous Multimedia. ACM Press, New York, 1--4. Google ScholarDigital Library
- G. Wood and M. Newborough. 2007. Energy-use information transfer for intelligent homes: Enabling energy conservation with central and local displays. Energy Buildings 39, 4, 495--503.Google ScholarCross Ref
- T. Wu and M. Srivastava. 2012. Low-cost appliance state sensing for energy disaggregation. In Proceedings of the 4th ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'12). ACM Press, New York, 53--55. Google ScholarDigital Library
- W. Yao-Jung, D. Dennis, and R. Francis. 2011. Co-simulation based building controls implementation with networked sensors and actuators. In Proceedings of the 3rd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building (BuildSys'11). ACM Press, New York, 55--60. Google ScholarDigital Library
- D. Yazar and A. Dunkels. 2009. Efficient application integration in ip-based sensor networks. In Proceedings of the 1st ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys'11). ACM Press, New York, 43--48. Google ScholarDigital Library
- 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. Sens. Netw. 5, 1--10. Google ScholarDigital Library
- T.-J. Yun. 2009. Investigating the impact of a minimalist in-home energy consumption display. In Proceedings of the 27th International Conference Extended Abstracts on Human Factors in Computing Systems (CHI'09). ACM Press, New York, 4417--4422. Google ScholarDigital Library
- M. Zeifman and K. Roth. 2011. Nonintrusive appliance load monitoring: Review and outlook. IEEE Trans. Consumer Electron. 57, 1, 76--84. Google ScholarDigital Library
- Y. Zhu. 2006. Applying computer-based simulation to energy auditing: A case study. Energy Buildings 38, 5, 421--428.Google ScholarCross Ref
- A. Zoha, A. Gluhak, M. A. Imran, and S. Rajasegarar. 2012. Non-intrusive load monitoring approaches for disaggregated energy sensing: A survey. Sensors 12, 12, 16838--16866.Google ScholarCross Ref
Index Terms
- A Review of Wireless-Sensor-Network-Enabled Building Energy Management Systems
Recommendations
Energy-Harvesting Wireless Sensor Networks (EH-WSNs): A Review
Wireless Sensor Networks (WSNs) are crucial in supporting continuous environmental monitoring, where sensor nodes are deployed and must remain operational to collect and transfer data from the environment to a base-station. However, sensor nodes have ...
Energy management in Wireless Sensor Networks
Energy management in Wireless Sensor Networks (WSNs) is of paramount importance for the remotely deployed energy stringent sensor nodes. These nodes are typically powered by attached batteries. Several battery-driven energy conservation schemes are ...
Prediction free energy neutral power management for energy harvesting wireless sensor nodes
Current power management mechanisms for energy harvesting wireless sensors typically rely on predicted information about the amount of energy that can be harvested in the future. However, such mechanisms suffer from inevitable prediction errors, which ...
Comments