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Publisher: Elsevier
Languages: English
Types: Article
Subjects: TA
Smart scheduling of energy consuming devices in the domestic sector should factor in clean energy\ud generation potential, electricity tariffs, and occupants’ behaviour (i.e. interactions with their\ud appliances). The paper presents an ANN–GA (Artificial Neural Network / Genetic Algorithm) smart\ud appliance scheduling approach for optimized energy management in the domestic sector. The\ud proposed approach reduces energy demand in “peak” periods, maximizes use of renewable sources\ud (PV and wind turbine), while reducing reliance on grid energy. Comprehensive parameter\ud optimization has been carried out for both ANN and GA to find the best combinations, resulting in\ud optimum weekly schedules. The proposed artificial intelligence techniques involve a holistic\ud understanding of (near) real-time energy demand and supply within a domestic context to deliver\ud optimized energy usage with minimum computational needs. The solution is stress-tested and\ud demonstrated in a four bedroom house with grid energy usage reduction by 10%, 25%, and 40%,\ud respectively.
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    • E.C., Challenging and Changing Europe's Built Environment: A Vision for a Sustainable and Competitive Construction Sector by 2030, European Construction Technology Platform (ECTP), 2005.
    • BERR, Digest of UK Energy Statistics, Department for Business Enterprise and Regulatory Reform, London, 2007, http://stats.berr.gov.uk/energystats/dukes07_c6.pdf. (Accessed: 24 September 2014).
    • M. Yilmaz, P.T. Krein, Review of the Impact of Vehicle-to-Grid Technologies on Distribution Systems and Utility Interfaces, IEEE Transaction of Power Electronics, 2013, vol. 28, pp. 5673- 5689. DOI:10.1109/TPEL.2012.22275007.
    • B. Yuce, M.S. Packianather, E. Mastrocinque, D.T. Pham and A. Lambiase, Honey bees inspired optimization method: the Bees Algorithm, Insects, 2013, vol. 44(4), pp. 646-662 ISSN 2075- 445010.3390/insects4040646.
    • B. Yuce, E. Mastrocinque, M.S. Packianather, D.T. Pham, A. Lambiase, F. Fruggiero, Neural network design and feature selection using principal component analysis and Taguchi method for identifying wood veneer defects, Production & Manufacturing Research: An Open Access Journal , 2014, vol.2 (1), pp. 291-308.
    • I. Petri, H. Li, Y. Rezgui, C. Yang, B. Yuce, B. Jayan, A HPC based cloud model for real-time energy optimisation, Enterprise Information Systems, 2014, pp.1-21. ISSN 2169- 3277 10.1080/21693277.2014.892442.
    • M. Dibley, H. Li, Y. Rezgui, J. Miles, An ontology framework for intelligent sensor-based building monitoring, Automation in Construction, 2012, vol. 28, pp. 1-14.
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