Forecasting the Brazilian demand for biodiesel using artificial neural networks
DOI:
https://doi.org/10.33448/rsd-v10i5.13381Keywords:
Biodiesel; Brazil; Artificial neural networks; Seasonality; Demand forecast.Abstract
Biodiesel is a renewable fuel used as an alternative to totally or partially replace petroleum diesel. The mandatory percentage of this biofuel added to fossil diesel in Brazil has constantly been increasing. Predicting the amount of biodiesel that will be demanded in the future is essential to maintain the national surplus balance and assist in the sector decision-making. Artificial neural networks (ANNs) help forecast different types of demands. Therefore, this study uses artificial neural networks to forecast the Brazilian demand for biodiesel. The ANN proposed in this work encompassed data obtained from a non-parametric demand forecasting model based on time series. The non-parametric model considered the trends and seasonality of the data to forecast the demand for biodiesel. One hundred multilayer perceptron networks were modeled with error propagation for two scenarios of Brazilian biodiesel (use of 15% (B15) or 20% (B20) of biodiesel to diesel). All values of R2 greater than 0.99 for the simulated networks and RMSE <2% prove that the RNA model developed has high precision in predicting the demand for biodiesel. The best network for each scenario was determined by RMSE heuristic analysis. The best-simulated RNA results showed growth in biodiesel demand from 2019 to 2050 of 150.63% for B15. And 229.73% for B20. Both demand growth scenarios are justified by the gradual increase in the mandatory percentage of biodiesel to diesel. Thus, the growing results of biodiesel demand prove the country search for a non-toxic, biodegradable, and renewable fuel in its energy matrix.
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