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• W4386373540 abstract "Most building and energy management system (BEMS) solutions follow a set of rules (supervised or unsupervised learning) to make energy-saving recommendations to inhabitants. However, these systems are normally solely trained on energy data meaning that they do not consider other key factors, such as the inhabitants’ comfort or preferences. The lack of adaption to inhabitants renders these energy-saving solutions largely ineffective. Moreover, BEMS solutions are cloud-based entailing greater cyberattack risks and a high data transmission load. To address these problems, this research proposes an edge computing architecture based on virtual organizations and distributed explainable artificial intelligence (XAI) algorithms for optimized energy use in buildings/homes and demand response. Thanks to virtual organizations’ energy efficiency (EE) measures, which consider the inhabitants’ comfort and dynamically learn from real-time inhabitant data, the consumption patterns of the inhabitants are effectively optimized." @default.
• W4386373540 created "2023-09-02" @default.
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• W4386373540 date "2023-01-01" @default.
• W4386373540 modified "2023-10-03" @default.
• W4386373540 title "Adaptive and Intelligent Edge Computing Based Building Energy Management System" @default.
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• W4386373540 doi "https://doi.org/10.1007/978-3-031-36957-5_4" @default.
• W4386373540 hasPublicationYear "2023" @default.
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