Titlebook: Artificial Intelligence Techniques for a Scalable Energy Transition; Advanced Methods, Di Moamar Sayed-Mouchaweh Book 2020 Springer Nature

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Ina Wagner,Tone Bratteteig,Dagny Stuedahl model developed and we also realize different factors analysis which may affect the energy consumption for optimization purposes. This leads in setting well the human interface to be sure that each user sticks to each advice in order to guarantee an efficient smart building energy management system

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https://doi.org/10.1007/978-1-84996-223-0e latest advances in artificial intelligence, offer a potential solution to this problem. However, these solutions are marred by data and computational requirements, as well as privacy concerns. Transfer learning has recently been shown to help avoid the requirement of copious amounts of data requir

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Michael Menichelli,Alessio Maria Bracciniutput of the energy conversion system are compared against the estimated outputs which are provided by the neural model. Thus, the residuals’ sequence is generated. It is shown that the sum of the squares of the residuals’ vectors, multiplied with the inverse of the associated covariance matrix, sta

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l), and to the application domain (demand side management, load monitoring, micro grids, energy consulting (residents, utilities), energy saving, dynamic pricing revenue management and smart meters, etc.)..978-3-030-42728-3978-3-030-42726-9

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