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Title	Advanced Deep Learning Algorithms For Energy Optimization Of Smart Cities
ID_Doc	6496
Authors	Rojek I.; Mikołajewski D.; Galas K.; Piszcz A.
Year	2025
Published	Energies, 18, 2
DOI	http://dx.doi.org/10.3390/en18020407
Abstract	Advanced deep learning algorithms play a key role in optimizing energy usage in smart cities, leveraging massive datasets to increase efficiency and sustainability. These algorithms analyze real-time data from sensors and IoT devices to predict energy demand, enabling dynamic load balancing and reducing waste. Reinforcement learning models optimize power distribution by learning from historical patterns and adapting to changes in energy usage in real time. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) facilitate detailed analysis of spatial and temporal data to better predict energy usage. Generative adversarial networks (GANs) are used to simulate energy usage scenarios, supporting strategic planning and anomaly detection. Federated learning ensures privacy-preserving data sharing in distributed energy systems, promoting collaboration without compromising security. These technologies are driving the transformation towards sustainable and energy-efficient urban environments, meeting the growing demands of modern smart cities. However, there is a view that if the pace of development is maintained with large amounts of data, the computational/energy costs may exceed the benefits. The article aims to conduct a comparative analysis and assess the development potential of this group of technologies, taking into account energy efficiency. © 2025 by the authors.
Author Keywords	artificial intelligence; deep learning; energetic optimization; energy management; Internet of Things (IoT); smart buildings; smart city

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