Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting

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 emis...

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Bibliographic Details
Published in:	Energies (Basel) Vol. 11; no. 4; p. 949
Main Authors:	Divina, Federico, Gilson, Aude, Goméz-Vela, Francisco, García Torres, Miguel, Torres, José
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01-04-2018
Subjects:	Carbon dioxide Case studies Economic forecasting Electricity Electricity consumption Energy consumption energy consumption forecasting Energy demand ensamble learning evolutionary computation Forecasting Learning neural networks regression Short term Stacking Teaching methods time series forecasting
Online Access:	Get full text
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Summary:	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.
ISSN:	1996-1073 1996-1073
DOI:	10.3390/en11040949