Forecasting short-term electricity consumption using a semantics-based genetic programming framework: The South Italy case

Mauro Castelli, Leonardo Vanneschi and Matteo De Felice

Energy Economics, 2015, vol. 47, issue C, 37-41

Abstract: 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.

Keywords: Forecasting; Electricity demand; Genetic programming; Semantics (search for similar items in EconPapers)
JEL-codes: C15 C18 C44 C45 C53 (search for similar items in EconPapers)
Date: 2015
References: View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:47:y:2015:i:c:p:37-41

DOI: 10.1016/j.eneco.2014.10.009

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