| 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 | |
| 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. |
| https://doi.org/10.1016/j.proeng.2014.02.086 |
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