On-line learning of predictive kernel models for urban water demand in a smart city

[EN] This paper proposes a multiple kernel regression (MKr) to predict water demand in the presence of a continuous source of infor- mation. 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.
This work has been supported by project IDAWAS, DPI2009- 11591, of the Direccion General de Investigacion of the Ministerio de Ciencia e Innovacion of Spain, and ACOMP/ 2011/ 188 of the Conselleria d'Educacio of the Generalitat Valenciana.