Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting

Federico Divina, Aude Gilson, Francisco Goméz-Vela, Miguel García Torres and José F. Torres
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Federico Divina: Division of Computer Science, Universidad Pablo de Olavide, ES-41013 Seville, Spain
Aude Gilson: Faculty of Computer Science, University of Namur, B-5000 Namur, Belgium
Francisco Goméz-Vela: Division of Computer Science, Universidad Pablo de Olavide, ES-41013 Seville, Spain
Miguel García Torres: Division of Computer Science, Universidad Pablo de Olavide, ES-41013 Seville, Spain
José F. Torres: Division of Computer Science, Universidad Pablo de Olavide, ES-41013 Seville, Spain

Energies, 2018, vol. 11, issue 4, 1-31

Abstract: The ability to predict short-term electric energy demand would provide several benefits, both at the economic and environmental level. For example, it would allow for an efficient use of resources in order to face the actual demand, reducing the costs associated to the production as well as the emission of CO 2 . To this aim, in this paper we propose a strategy based on ensemble learning in order to tackle the short-term load forecasting problem. In particular, our approach is based on a stacking ensemble learning scheme, where the predictions produced by three base learning methods are used by a top level method in order to produce final predictions. We tested the proposed scheme on a dataset reporting the energy consumption in Spain over more than nine years. The obtained experimental results show that an approach for short-term electricity consumption forecasting based on ensemble learning can help in combining predictions produced by weaker learning methods in order to obtain superior results. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that using an ensemble scheme can achieve very accurate predictions, and thus that it is a suitable approach for addressing the short-term load forecasting problem.

Keywords: ensamble learning; time series forecasting; energy consumption forecasting; evolutionary computation; neural networks; regression (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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