Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting
| Autor: | Francisco Gómez-Vela, Aude Gilson, Miguel García Torres, Federico Divina, José F. Torres |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Mathematical optimization
Control and Optimization Computer science 020209 energy Energy Engineering and Power Technology 02 engineering and technology lcsh:Technology Evolutionary computation energy consumption forecasting 0202 electrical engineering electronic engineering information engineering Production (economics) time series forecasting Electrical and Electronic Engineering Time series Engineering (miscellaneous) ensamble learning evolutionary computation neural networks regression Artificial neural network Renewable Energy Sustainability and the Environment business.industry lcsh:T Energy consumption Ensemble learning Term (time) 020201 artificial intelligence & image processing Electricity business Energy (miscellaneous) |
| Zdroj: | Energies; Volume 11; Issue 4; Pages: 949 Energies, Vol 11, Iss 4, p 949 (2018) |
| ISSN: | 1996-1073 |
| DOI: | 10.3390/en11040949 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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