| Title |
On-line learning of predictive kernel models for urban water demand in a smart city |
| ID_Doc |
41941 |
| Authors |
Herrera, M; Izquierdo, J; Pérez-García, R; Ayala-Cabrera, D |
| Title |
On-line learning of predictive kernel models for urban water demand in a smart city |
| Year |
2014 |
| Published |
|
| DOI |
10.1016/j.proeng.2014.02.086 |
| Abstract |
This paper proposes a multiple kernel regression (MKr) to predict water demand in the presence of a continuous source of information. MKr extends the simple support vector regression (SVR) to a combination of kernels from as many distinct types as kinds of input data are available. In addition, two on-line learning methods to obtain real time predictions as new data arrives to the system are tested by a real-world case study. The accuracy and computational efficiency of the results indicate that our proposal is a suitable tool for making adequate management decisions in the smart cities environment. |
| Author Keywords |
Smart cities; urban water demand; kernel regression; on-line learning |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000341500600086 |
| WoS Category |
Automation & Control Systems; Engineering, Multidisciplinary; Water Resources |
| Research Area |
Automation & Control Systems; Engineering; Water Resources |
| PDF |
https://doi.org/10.1016/j.proeng.2014.02.086
|