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• W3088736418 abstract "In a context where smart grids are emerging, energy management strategies play a major role in tackling different challenges facing their development. This paper proposes a predictive supervision strategy to manage the activation of Electric Vehicles and Electric Water Heaters within a distribution grid. To do so, the energy production and consumption are forecasted using Artificial Neural Networks based on historical measurements and meteorological data. Then, these forecasts are used to determine controllable load profiles that minimize energy transmission costs. The system's performance is finally tested through simulation using real-time measurements collected from a distribution substation, showing an important reduction in energy transmission costs and also an increase in local renewable energy consumption." @default.
• W3088736418 created "2020-10-01" @default.
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• W3088736418 date "2020-09-01" @default.
• W3088736418 modified "2023-10-11" @default.
• W3088736418 title "Predictive Supervision Strategy for Demand-Side Management in Distribution Grids" @default.
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• W3088736418 doi "https://doi.org/10.1109/sest48500.2020.9203277" @default.
• W3088736418 hasPublicationYear "2020" @default.
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