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

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

Automated summary

Smart cities aim to reduce wasted energy, improve grid stability, and meet service demand by adopting next-generation energy systems and leveraging artificial intelligence, IoT, and communication technologies to collect and analyze real-time big data. However, training machine learning algorithms for energy-related tasks in sustainable smart cities is a challenging data science task. Transfer learning has been proposed as a promising solution to address these challenges.