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Yuce, Baris; Rezgui, Yacine; Mourshed, Monjur (2016)
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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