In the 21st century, energy conservation has been an important issues needs to be solved to create better environment. Since household area uses more than a quarter of total energy in a country, it is necessary to reduce the energy consumption in the house. A way to achieve this is by increasing the inhabitants’ awareness about the energy they have been using. The smart display used to provide energy information to the inhabitants is called magic mirror, which is located in the bathroom. It can be used by the user to reflect how much energy has been used in a particular time-frame and also convey useful recommendation to the user so they will have a better energy conservation lifestyle.
A lot of data will be involved to produce the most proper information. The semantic web approach will be used to model the data into ontology. The ontology has reasoning capabilities to help create a better recommendation and also has specific query language called SPARQL which is powerful to manipulate the data within the ontology. Finally, the ontology, SPARQL, and reasoning are implemented in PHP programming language to show that this semantic web based approach has the aptitude to create the intelligent bathroom.

In the 21st century, energy conservation has been an important issues needs to be solved to create better environment. Since household area uses more than a quarter of total energy in a country, it is necessary to reduce the energy consumption in the house. A way to achieve this is by increasing the inhabitants’ awareness about the energy they have been using. The smart display used to provide energy information to the inhabitants is called magic mirror, which is located in the bathroom. It can be used by the user to reflect how much energy has been used in a particular time-frame and also convey useful recommendation to the user so they will have a better energy conservation lifestyle.
A lot of data will be involved to produce the most proper information. The semantic web approach will be used to model the data into ontology. The ontology has reasoning capabilities to help create a better recommendation and also has specific query language called SPARQL which is powerful to manipulate the data within the ontology. Finally, the ontology, SPARQL, and reasoning are implemented in PHP programming language to show that this semantic web based approach has the aptitude to create the intelligent bathroom.

1. EIA, Annual Energy Outlook 2010, 2010, Washington, D.C: U.S Department of Energy, Energy Information Administration.
2. Darby, S., The Effectiveness of Feedback on Energy Consumption, 2006, Environmental Change Institute: University of Oxford.
3. Weidema, B.P., et al., Carbon Footprint A Catalyst for Life Cycle Assessment? Journal of Industrial Ecology, 2008. 12(1).
4. EnergieNed, National survey on natural gas use in households in 2000, 2001, Arnhem.
5. Bouwman, M.E., Tracking transport systems, an environmental perspective on passenger transport modes, 2000, University of Groningen.
6. Noorman, K.J. and T.S. Uiterkamp, Green Households?: Domestic Consumers, Environment, and Sustainability, 1998, London: Earthscan.
7. Parker, D.S., D. Hoak, and J. Cummings, Pilot Evaluation of Energy Savings from Residential Energy Demand Feedback Devices, 2008, Florida Solar Energy Center: United States.
8. Steg, L., Promoting household energy conservation. Energy Policy, 2008. 36: p. 4449–4453.
9. Benders, R., et al., New approaches for household energy conservation—In search of personal household energy budgets and energy reduction options. Energy Policy, 2006. 34.
10. Kok, R., R. Benders, and H. Moll, Measuring the environmental load of household consumption using some methods based on input–output energy analysis: A comparison of methods and a discussion of results. Energy Policy, 2006. 34(17): p. 2744–2761.
11. Ehrhardt-Martinez, K., Beyond the Meter: Enabling Better Home Energy Management, in Energy, Sustainability and the Environment2011, Elsevier. p. 273.
12. Mullaly, C., Home energy use behaviour: a necessary component of successful local government home energy conservation (LGHEC) programs. Energy Policy, 1999. 26(14): p. 1041-1052.
13. Kelso, J., 2009 Buildings Energy Data Book, 2009, D&R International, Ltd.: Maryland.
14. Maslin, M., et al., UK Greenhouse emissions: Are We on Target?, 2007, Environment Institute: London.
15. Scott, S., Household energy efficiency in Ireland: a replication study of owner of energy saving items. Energy Economics, 1997. 19: p. 187–208.
16. Ek, K. and P. Soderholm, The devil is in the details: Household electricity saving behavior and the role of information. Energy Policy, 2010. 38(3): p. 1578–1587.
17. Mills, B. and J. Schleich, Residential energy-efficient technology adoption, energy conservation, knowledge, and attitudes: Ananalysis o fEuropean countries. Energy Policy, 2012. 49: p. 616-628.
18. Lofstrom, E. and J. Palm, Visualizing energy used in households to develop sustainable energy systems – different methods, in 13 Housing and urban processes: towards sustainable communities?2006: Ljubljana.
19. Wood, G. and M. Newborough, Energy-use information transfer for intelligent homes: Enabling energy conservation with central and local displays. Energy and Buildings, 2006. 39(2007): p. 495-503.
20. Kaplowitz, M.D., et al., Energy conservation attitudes, knowledge, and behaviors in science laboratories. Energy Policy, 2012. 50: p. 581-591.
21. Midden, C.J.H., F.G. Kaiser, and L.T. McCalley, Technology's Four Roles in Understanding Individuals' Conservation of Natural Resources. Journal of Social Issues, 2007. 63(1): p. 155 - 174.
22. Ehrhardt-Martinez, K., K. Donnelly, and J. Laitner, Advanced Metering Initiatives and Residential Feedback Programs: A Meta-Review for Household Electricity-Saving Opportunities, 2010: Washington, D.C.
23. Rahmana, F., et al., Design and implementation of an open framework for ubiquitous carbon footprint calculator applications. Sustainable Computing: Informatics and Systems, 2011. 1: p. 257-274.
24. Cook, D. and S. Das, Smart Environments: Technology, Protocols and Applications2004: John Wiley & Sons.
25. Hopper, A., Sentient Computing?, 1999, Royal Society Clifford Paterson Lecture. p. 2349-2358.
26. Kofler, M.J., C. Reinisch, and W. Kastner, An Intelligent Knowledge Representation of Smart Home Energy Parameters, in World Renewable Energy Congress 20112011: Sweden.
27. Nguyen, T.V., et al., Context Ontology Implementation for Smart Home, Department Of Computer Engineering, Chonnam National University.
28. Chen, L., et al., Semantic Smart Homes: Towards Knowledge Rich Assisted Living Environments, University of Ulster.
29. Chou, S.Y., et al., Energy Conservation in Intelligent Bathroom based on Information Stimulation, 2014, National Taiwan University of Science and Technology.
30. Weiser, M., The Computer for the 21st Century. Scientific American, 1991. 265(3): p. 94-104.
31. Zhang, D., T. Gu, and X. Wang. Enabling Context-aware Smart Home with Semantic Web Technologies. Institute for Infocomm Research.
32. Gomez-Perez, A., M. Fernandez-Lopez, and O. Corcho, Ontological Engineering: with examples from the areas of Knowledge Management, e-Commerce and the Semantic Web. Information Systems and Applications2004, New York: Springer Verlag.
33. Noy, N.F. and D.L. McGuinness, Ontology Development 101: A Guide to Creating Your First Ontology, Stanford University: United States.
34. Bonino, D. and F. Corno, DogOnt - Ontology Modeling for Intelligent Domotic Environments, 2008, Springer-Verlag. p. 790–803.