Forecasting biofuel production using adaptive integrated optimization network model

doi:10.1016/j.fuel.2020.118764

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Forecasting biofuel production using adaptive integrated optimization network model

2021-01-01

PRE

Naveen Kumar *

S. Vijayabaskar

L. Murali

DOI：10.1016/j.fuel.2020.118764

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Abstract

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Recently, biofuel production has been increasing all over the world over the past few decades owing to the relative changes in energy price and policy intervention. Several research studies are focussed on producing optimal biofuel production using numerous approaches. However, those approaches have few drawbacks such as computational complexity, less accuracy, high error value rate, etc. So to overcome such shortcomings, this paper aims in developing a novel Adaptive Integrated Optimization Network (AION approach) to attain optimal biofuel production with high accuracy and minimum error value rate. Moreover, the proposed AION approach comprises four significant phases namely Pre-processing of data, Re-construction of component, Prediction of an individual and Ensemble forecasting. The initial process utilizes an empirical mode decomposition (EMD) approach to divide a raw complicated data. The reconstruction phase employs the Fine to coarse (FTC) reconstructing approach that rebuilds the I-MF into two different t-testing components namely the low-frequency component and the high-frequency component. Moreover, Gray Wolf Optimization (GWO) based Adaptive Wavelet Neural Network (AWNN) and Long Short Term Memory-Recurrent Neural Network (LSTM-RNN) approaches are applied to predict the low and the high-frequency components. Then the forecasted results are summed by using an ADD operator so as to obtain absolute forecasting results. The experimental analysis reveals that the proposed AION approach provides an optimal biofuel production with enhanced accuracy, minimum error value rate, and complexity issues.

Keywords:

Biofuel production

GWO

LSTM-RNN

AWNN

EMD

FTC

Forecasting

AION

High-frequency component

Low-frequency component

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