Research Article

BiGRU-CNN neural network applied to short-term electric load forecasting

Lucas Duarte Soares; Edgar Manuel Carreño Franco

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Paper aims: This study analyzed the feasibility of the BiGRU-CNN artificial neural network as a forecasting tool for short-term electric load. This forecasting model can serve as a support tool related to decision-making by companies in the energy sector.

Originality: Despite a large amount of scientific research in this area, the literature still searches for more assertive forecasting models regarding short-term electric load. Thus, the BiGRU-CNN model, based on layers of BiGRU and CNN architecture networks was tested. This model was already proposed and used for other similar tasks, however, it has not been used on load forecasting.

Research method: The code was programmed in Python using the keras package. The forecasts of all networks were carried out 10 times until an acceptable statistical sample was reached so that future electric load values are as close as possible to reality.

Main findings: The best forecasting model was the proposed BiGRU-CNN network when compared to classical and some hybrid networks.

Implications for theory and practice: This methodology can be applied to short-term electric load forecasting problems. There is evidence that the combination of different layers of neural networks can provide more efficient forecasting results than classical networks with only one architecture.


Time series forecasting, Recurrent neural networks, Artificial intelligence, Machine learning


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