| Title |
Prediction of Machine Learning Base for Efficient Use of Energy Infrastructure in Smart City |
| ID_Doc |
36085 |
| Authors |
Yoon, G; Park, S; Park, S; Lee, T; Kim, S; Jang, H; Lee, S; Park, S |
| Title |
Prediction of Machine Learning Base for Efficient Use of Energy Infrastructure in Smart City |
| Year |
2019 |
| Published |
|
| DOI |
10.1109/iccece46942.2019.8941864 |
| Abstract |
The 21st century, which we live in today, is rapidly changing based on the fourth industrial revolution and information and communication technology. According to change, existing urban models are emerging as smart cities. Smart City is a sustainable city that improves urban functions of various elements by applying information and communication convergence technology and environment friendly technology to urban space and solves urban problems that are occurring now. Energy, a core element of the city, enables efficient energy management of energy systems through an Internet of Things-based platform. The Internet of Things is a technology that sends and receives data by attaching sensors to various objects through a network. The goal is to use resources efficiently as the Internet of Things is spread and various industries are applied. This paper is a prediction study based on machine learning algorithms for efficient use of energy infrastructure in the Smart City. The proposed system builds the optimal Smart City by estimating energy efficiency of the Smart City and improving resource utilization. |
| Author Keywords |
Machine learning; Data analysis; Smart City; Energy; Internet of Things; Big data |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000525754800007 |
| WoS Category |
Computer Science, Theory & Methods; Engineering, Electrical & Electronic |
| Research Area |
Computer Science; Engineering |
| PDF |
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