Utilize este identificador para referenciar este registo: http://hdl.handle.net/10362/28070
Título: Forecasting short-term electricity consumption using a semantics-based genetic programming framework
Autor: Castelli, Mauro
Vanneschi, Leonardo
De Felice, Matteo
Data: 1-Jan-2015
Resumo: Accurate and robust short-term load forecasting plays a significant role in electric power operations. This paper proposes a variant of genetic programming, improved by incorporating semantic awareness in algorithm, to address a short term load forecasting problem. The objective is to automatically generate models that could effectively and reliably predict energy consumption. The presented results, obtained considering a particularly interesting case of the South Italy area, show that the proposed approach outperforms state of the art methods. Hence, the proposed approach reveals appropriate for the problem of forecasting electricity consumption. This study, besides providing an important contribution to the energy load forecasting, confirms the suitability of genetic programming improved with semantic methods in addressing complex real-life applications. (C) 2014 Elsevier B.V. All rights reserved.
Descrição: Castelli, M., Vanneschi, L., & De Felice, M. (2015). Forecasting short-term electricity consumption using a semantics-based genetic programming framework: The South Italy case. Energy Economics, 47(NA), 37-41. DOI: 10.1016/j.eneco.2014.10.009
Peer review: yes
URI: http://hdl.handle.net/10362/28070
DOI: http://dx.doi.org/10.1016/j.eneco.2014.10.009
ISSN: 0140-9883
Aparece nas colecções:NIMS: MagIC - Artigos em revista internacional com arbitragem científica

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