RT Journal Article
T1 Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting
A1 Divina, Federico
A1 Gilson, Aude
A1 Gómez-Vela, Francisco Antonio
A1 García Torres, Miguel
A1 Torres Maldonado, José Francisco
K1 Ensamble learning
K1 Time series forecasting
K1 Energy consumption forecasting
K1 Evolutionary computation
K1 Neural networks
K1 Regression
AB 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 CO2. 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.
PB MDPI
YR 2018
FD 2018-04-18
LK https://hdl.handle.net/10433/20452
UL https://hdl.handle.net/10433/20452
LA en
NO Energies 2018, 11(4), 949; https://doi.org/10.3390/en11040949
NO Universidad Pablo de Olavide de Sevilla
DS RIO
RD May 9, 2026