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• W4229075479 abstract "In the energy sector, prosumers are becoming relevant entities for energy management systems since they can share energy with their citizen energy community (CEC). Thus, this paper proposes a novel methodology based on demand response (DR) participation in a CEC context, where unsupervised learning algorithms such as convolutional neural networks and k-means are used. This novel methodology can analyze future events on the grid and balance the consumption and generation using end-user flexibility. The end-users’ invitations to the DR event were according to their ranking obtained through three metrics. These metrics were energy flexibility, participation ratio, and flexibility history of the end-users. During the DR event, a continuous balancing assessment is performed to allow the invitation of additional end-users. Real data from a CEC with 50 buildings were used, where the results demonstrated that the end-users’ participation in two DR events allows reduction of energy costs by EUR 1.31, balancing the CEC energy resources." @default.
• W4229075479 created "2022-05-08" @default.
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• W4229075479 date "2022-03-24" @default.
• W4229075479 modified "2023-10-13" @default.
• W4229075479 title "Evaluation Metrics to Assess the Most Suitable Energy Community End-Users to Participate in Demand Response" @default.
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• W4229075479 doi "https://doi.org/10.3390/en15072380" @default.
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