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On-line Learning of Predictive Kernel Models for Urban Water Demand in a Smart City.

Authors :
Herrera, M.
Izquierdo, J.
Pérez-Garćıa, R.
Ayala-Cabrera, D.
Source :
Procedia Engineering; Apr2014, Vol. 70, p791-799, 9p
Publication Year :
2014

Abstract

Abstract: 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. [Copyright &y& Elsevier]

Details

Language :
English
ISSN :
18777058
Volume :
70
Database :
Supplemental Index
Journal :
Procedia Engineering
Publication Type :
Academic Journal
Accession number :
95724574
Full Text :
https://doi.org/10.1016/j.proeng.2014.02.086