This paper presents an assessment of the potential behind the BiGRU-CNN artificial neural network to be used as an electric power theft detection tool. The network is based on different architecture layers of the bidirectional gated recurrent unit and convolutional neural network. The use of such a tool with this classification model can help energy sector companies to make decisions regarding theft detection. The BiGRU-CNN artificial neural network singles out consumer units suspected of fraud for later manual inspections. The proposed artificial neural network was programmed in python, using the keras package. The best detection model was that of the BiGRU-CNN artificial neural network when compared to multilayer perceptron, recurrent neural network, gated recurrent unit, and long short-term memory networks. Several tests were carried out using data of an actual electricity supplier, showing the effectiveness of the proposed approach. The metric values assigned to their classifications were 0.929 for accuracy, 0.885 for precision, 0.801 for recall, 0.841 for F1-Score, and 0.966 for area under the receiver operating characteristic curve.

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.

A energia elétrica é uma das formas de energia mais utilizada no mundo. Sendo de grande importância para o crescimento de qualquer economia no mundo. A biomassa gerada pelo setor sucroalcooleiro, que produz açúcar e álcool, pode ter uma contribuição significativa na matriz energética brasileira. Neste contexto, este trabalho tem como objetivo realizar uma previsão de demanda, através do método de Box-Jenkins, do consumo de cana de açúcar, baseado em dados obtidos do setor energético brasileiro. A obtenção do modelo de previsão baseou-se na análise de gráficos e testes estatísticos. Adotou-se, neste trabalho, o modelo ARIMA(2,1,1) para prever o consumo de bagaço de cana de açúcar.

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