Advanced Deep Learning Algorithms for Energy Optimization of Smart Cities
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 redu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/2/407 |
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| _version_ | 1832588569655377920 |
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| author | Izabela Rojek Dariusz Mikołajewski Krzysztof Galas Adrianna Piszcz |
| author_facet | Izabela Rojek Dariusz Mikołajewski Krzysztof Galas Adrianna Piszcz |
| author_sort | Izabela Rojek |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9e1ae843093341e5ae7f3454c0f55e3a |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-9e1ae843093341e5ae7f3454c0f55e3a2025-01-24T13:31:23ZengMDPI AGEnergies1996-10732025-01-0118240710.3390/en18020407Advanced Deep Learning Algorithms for Energy Optimization of Smart CitiesIzabela Rojek0Dariusz Mikołajewski1Krzysztof Galas2Adrianna Piszcz3Faculty of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, PolandFaculty of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, PolandFaculty of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, PolandFaculty of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, PolandAdvanced 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.https://www.mdpi.com/1996-1073/18/2/407artificial intelligencedeep learningsmart citysmart buildingsInternet of Things (IoT)energy management |
| spellingShingle | Izabela Rojek Dariusz Mikołajewski Krzysztof Galas Adrianna Piszcz Advanced Deep Learning Algorithms for Energy Optimization of Smart Cities Energies artificial intelligence deep learning smart city smart buildings Internet of Things (IoT) energy management |
| title | Advanced Deep Learning Algorithms for Energy Optimization of Smart Cities |
| title_full | Advanced Deep Learning Algorithms for Energy Optimization of Smart Cities |
| title_fullStr | Advanced Deep Learning Algorithms for Energy Optimization of Smart Cities |
| title_full_unstemmed | Advanced Deep Learning Algorithms for Energy Optimization of Smart Cities |
| title_short | Advanced Deep Learning Algorithms for Energy Optimization of Smart Cities |
| title_sort | advanced deep learning algorithms for energy optimization of smart cities |
| topic | artificial intelligence deep learning smart city smart buildings Internet of Things (IoT) energy management |
| url | https://www.mdpi.com/1996-1073/18/2/407 |
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