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

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書目名稱Artificial Intelligence Techniques for a Scalable Energy Transition影響因子(影響力)




書目名稱Artificial Intelligence Techniques for a Scalable Energy Transition影響因子(影響力)學科排名




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書目名稱Artificial Intelligence Techniques for a Scalable Energy Transition網絡公開度學科排名




書目名稱Artificial Intelligence Techniques for a Scalable Energy Transition被引頻次




書目名稱Artificial Intelligence Techniques for a Scalable Energy Transition被引頻次學科排名




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書目名稱Artificial Intelligence Techniques for a Scalable Energy Transition年度引用學科排名




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Large-Scale Building Thermal Modeling Based on Artificial Neural Networks: Application to Smart Ener 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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Automated Demand Side Management in Buildingse 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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Using Model-Based Reasoning for Self-Adaptive Control of Smart Battery Systemsphysical model for fault detection and a logical model for computing the root cause of the observed failure. The intention behind the chapter is to provide all necessary details of the methods allowing to adapt the methods to implement similar smart adaptive systems.

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Data-Driven Predictive Flexibility Modeling of Distributed Energy Resourcestly unknown and uncertain, and (3) lack of available behind-the-meter sensing and measurements (partly due privacy concerns). As such, data-driven deep learning based frameworks have been proposed in this work to identify aggregated predictive flexibility models of a collection of DERs, using front-

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