Energy Wastage Detection in Smart Buildings

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dc.contributor.authorSifat, Samiun-Raji
dc.contributor.authorShad, Nabil
dc.date.accessioned2021-10-06T04:58:34Z
dc.date.available2021-10-06T04:58:34Z
dc.date.issued2017-11-15
dc.descriptionSupervised by Ashraful Alam Khan, Lecturer, Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh.en_US
dc.description.abstractEnergy is wasted due to unconsciousness and negligence of the users. Buildings are the major source of energy consumption. So, most of the energy wastage detection techniques are designed for buildings. In our proposed method we use the occupancy based sensors for the detection of the unnecessary consumption. Along with current data, the stored data is also used for the decision making. To attain accuracy and efficiency, the reinforcement learning algorithm is used in data processing. A user interface is there to take the user feedback. This user feedback is used for the learning growth of reinforcement learning algorithm.en_US
dc.identifier.citation1. http://www.buildings.com/article-details/articleid/19537/title/how-smart-buildings-save-energy.aspx 2. American Council for an Energy Efficient Economy. 3. http://timesofindia.indiatimes.com/india/Three-billion-units-of-power-wasted-in-one-year/articleshow/47942237.cms 4. https://en.m.wikipedia.org/wiki/Electricity_sector_in_Bangladesh 5. R. Fontugne, J. Ortiz, N. Tremblay, P. Borgnat, P. Flandrin, K. Fukuda, D. Culler, and H. Esaki, “Strip, bind, and search : a Method for identifying abnormal energy consumption in buildings,” 12th International Conference on Information Processing in Sensor Networks, pp. 129–140, 2013. 6. Araya, Daniel B., et al. "Collective contextual anomaly detection framework for smart buildings." Neural Networks (IJCNN), 2016 International Joint Conference on. IEEE, 2016. 7. Miller, Clayton, Zoltán Nagy, and Arno Schlueter. "Automated daily pattern filtering of measured building performance data." Automation in Construction 49 (2015): 1-17.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/1094
dc.language.isoenen_US
dc.publisherDepartment of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladeshen_US
dc.titleEnergy Wastage Detection in Smart Buildingsen_US
dc.typeThesisen_US

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