| Title |
3D Modeling of Urban Environment for Efficient Renewable Energy Production in the Smart City |
| ID_Doc |
37116 |
| Authors |
Anbari, S; Majidi, B; Movaghar, A |
| Title |
3D Modeling of Urban Environment for Efficient Renewable Energy Production in the Smart City |
| Year |
2019 |
| Published |
|
| DOI |
10.1109/cfis.2019.8692154 |
| Abstract |
One of the major challenges faced by the humanity in the next century is the climate change and global warming. Climate change is caused by the rapid emission of greenhouse gases. Energy production industry is facing the challenge of providing the energy require by the rapidly expanding need of urbanized societies while reducing their emission. Clean and renewable energy sources such as solar and wind energy can gradually replace the energy sources with high emission. Smart cities can provide a major part of their energy requirements using these clean energy sources. The energy sector should be able to make decisions about the best scenarios for deployment and placement of clean energy production devices such as solar panels and wind turbines. In this paper,3D modelling of the urban environment is used for efficient placement of solar panels in the urban environment for optimum electricity production. Furthermore, a model for prediction of the speed of the wind using deep neural networks is proposed. The proposed solar panel placement framework is simulated on a part of the city of Tehran, Iran. The proposed wind energy production framework is simulated on 16 wind energy production stations in Iran. The experimental results show that the combined use of the solar and wind energy sources in the smart city environment can help the energy companies to be able to answer the energy demands of the cities while reducing their cost and emission. |
| Author Keywords |
3D modelling; Computer simulation; Renewable energy; Deep learning; Smart city |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000493557900003 |
| WoS Category |
Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic |
| Research Area |
Computer Science; Engineering |
| PDF |
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