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Electricity Demand Forecasting by Multi-Task Learning

Authors
  • Fiot, Jean-Baptiste
  • Dinuzzo, Francesco
Publication Date
Jan 01, 2016
Source
HAL-UPMC
Keywords
Language
English
License
Unknown
External links

Abstract

We explore the application of kernel-based multi-task learning techniques to forecast the demand of electricity measured on multiple lines of a distribution network. We show that recently developed output kernel learning techniques are particularly well suited to solve this problem, as they allow to flexibly model the complex seasonal effects that characterize electricity demand data, while learning and exploiting correlations between multiple demand profiles. We also demonstrate that kernels with a multiplicative structure yield superior predictive performance with respect to the widely adopted (generalized) additive models. Our study is based on residential and industrial smart meter data provided by the Irish Commission for Energy Regulation (CER).

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