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Electric Energy Demand Forecasting in an Oil Production Company Using Artificial Neural Networks

Abstract:

Electric energy demand forecasting is vital for the correct and efficient operation of any industry, especially for those ones that need to purchase energy. Activities such as maintenance, new installations, and energy purchase, are planned based on the demand forecasting. Therefore, it is necessary to use an advanced technique to obtain a forecasting as accurate as possible. In the present paper, a methodology based on artificial neural networks is developed for forecasting the electric energy demand in an oil production company in Ecuador, based on fluid production and historical data. The forecasting technique was applied using Python programming language. Finally, the results are compared with the conventional simple linear regression methodology, proving a satisfactory precision.