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Published byMadlyn Austin Modified over 9 years ago
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Adaptive Control of House Environment - Neural Network House Presented by Wenjie Zeng
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The Main Idea – Why Adaptive Control ? Smart houses aren’t smart –Difficult to customize –Complex interface baffles inhabitants –Inhabitants just forget to start the “automation” Adaptive house –Based on inhabitants’ lifestyle –Self-configuration, learning through mistakes –Change itself when inhabitants’ lifestyle changes
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The Main Achievements – Optimal Control What did this work accomplish? – Air and water temperature regulation – Lighting (e.g. sleeping, reading, watching TV) – Neural nodes communication and coordination What are the contributions? – A mechanism to anticipate inhabitants’ needs – Saving energy (water not fixed at a temperature) – Optimal Control Model : Cost = Discomfort + Energy
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The Challenges – How to predict Is inhabitants’ life pattern sufficiently regular ? Butterfly effect – Wrong decision -> future state - > future decision -> … Inhabitants have to get through the learning process of the adaptive system Example : Occupancy Model
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Pictures Actual Deployment
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Innovation Real Smart - Minimum human interaction Self-configuration through time Optimal Control method – event-based decision making
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