Next-generation energy systems for sustainable smart cities: Roles of transfer learning

نویسندگان

چکیده

Smart cities attempt to reach net-zero emissions goals by reducing wasted energy while improving grid stability and meeting service demand. This is possible adopting next-generation systems, which leverage artificial intelligence, the Internet of things (IoT), communication technologies collect analyze big data in real-time effectively run city services. However, training machine learning algorithms perform various energy-related tasks sustainable smart a challenging science task. These might not as expected, take much time training, or do have enough input generalize well. To that end, transfer (TL) has been proposed promising solution alleviate these issues. best authors’ knowledge, this paper presents first review applicability TL for systems well-defined taxonomy existing frameworks. Next, an in-depth analysis carried out identify pros cons current techniques discuss unsolved Moving on, two case studies illustrating use (i) prediction with mobility (ii) load forecasting sports facilities are presented. Lastly, ends discussion future directions.

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ژورنال

عنوان ژورنال: Sustainable Cities and Society

سال: 2022

ISSN: ['2210-6707', '2210-6715']

DOI: https://doi.org/10.1016/j.scs.2022.104059