Prediction of CO<sub>2</sub> Emissions Using an Artificial Neural Network: The Case of the Sugar Industry

Saleh, Chairul; Leuveano, Raden Achmad Chairdino; Rahman, Mohd Nizam Ab; Deros, Baba Md; Dzakiyullah, Nur Rachman

doi:10.1166/asl.2015.6488

Cited by 8 publications

(3 citation statements)

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“…Furthermore, using ML models for conventional energy systems and alternative and renewable energy systems was promising and valuable in the study of Voyant et al (2017). Saleh et al (2015) reported that their findings could even be practical and helpful in industrial performance, especially for executives to make effective decisions for business performance by considering the costs of CO2 monitoring.…”

Section: Figure 12mentioning

confidence: 99%

Ecological Footprint Prediction based on Global Macro Indicators in G-20 Countries using Machine Learning Approaches

Roumiani

Mofidi

2021

Preprint

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Paying attention to human activities in terms of land grazing infrastructure, crops, forest products and carbon impact, the so-called ecological impact (EF) is one of the most important economic issues in the world. In the present study, data from global databases were used. The ability of the penalized regression approach (PR including Ridge, Lasso and Elastic Net) and artificial neural network (ANN) to predict EF indices in the G-20 over the past two decades (1999–2018) was depicted and compared. For this purpose, 10-fold cross-validation was used to assess predictive performance and to specify a penalty parameter for PR models. Based on the results, a slight improvement in prediction performance was observed over linear regression. Using the Elastic Net model, more global macro indices were selected than Lasso. Although Lasso included only some indicators, it still had better predictive performance among PR models. Although the findings using PR methods were only slightly better than linear regression, their interest in selecting a subset of controllable indicators by shrinking the coefficients and creating a parsimonious model was apparent. As a result, penalized regression methods would be preferred, using feature selectivity and interpretive considerations rather than predictive performance alone. On the other hand, neural network-based models with higher values of coefficients of determination (R2) and values lower of RMSE than PR and OLS had significant performance and showed that they are more accurate in predicting EF. The results showed that the ANN network could provide considerable and appropriate predictions for EF indicators in the G-20 countries. predictions

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Section: Figure 12mentioning

confidence: 99%

Ecological Footprint Prediction based on Global Macro Indicators in G-20 Countries using Machine Learning Approaches

Roumiani

Mofidi

2021

Preprint

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“…Literature on the use of ANN for modeling the highly nonlinear relationships among variables connected with emissions resulting from combustion of fuels that generate the heat needed to raise the enthalpy of water in boilers are also available (Ilamathi et al, 2013;Ronquillo-Lomeli et al, 2018;Saleh et al, 2015;Sun et al, 2013;Yusoff and Aziz, 2009). Using ANN, Yusoff and Aziz (2009) modeled emissions from the boiler of a palm oil mill that runs on shells and fibres which are waste from oil palm processing.…”

Section: Identification Modeling and Predictionmentioning

confidence: 99%

Artificial Neural Network and Its Applications in the Energy Sector – An Overview

Babatunde

Anozie

Omoleye

2020

IJEEP

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In order to realize the goal of optimal use of energy sources and cleaner environment at a minimal cost, researchers; field professionals; and industrialists have identified the expediency of harnessing the computational benefits provided by artificial intelligence (AI) techniques. This article provides an overview of AI, chronological blueprints of the emergence of artificial neural networks (ANNs) and some of its applications in the energy sector. This short survey reveals that despite the initial hiccups at the developmental stages of ANNs, ANN has tremendously evolved, is still evolving and have been found to be effective in handling highly complex problems even in the areas of modeling, control, and optimization, to mention a few.

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“…Findings indicate that the use of Root Mean Square Error produced a small error which makes the model more accurate in terms of predictions. Saleh, et al [36] also undertook a similar study in Indonesia sugar industry using a back-propagation neural network model with trial and error approach. The study was based on the recent trend of emissions in Indonesia which was reported to emanate from sugar industry in their operation process through fuel used in boilers as well as the electricity use, natural gas and solar energy used.…”

Section: Introduction mentioning

confidence: 99%

Prediction of Potential Carbon Dioxide Emissions of Selected Emerging Economies Using Artificial Neural Network

Appiah¹,

Du²,

Appah³

et al. 2018

JESE-A

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Tackling future global emissions of carbon dioxide is a daunting task. Different black box models have been used to determine the trajectories of CO 2 emissions and other carbon stocks. Trajectories are important because climate modelers use them to project future climate under higher atmospheric CO 2 concentrations. In this paper, fully connected two-layer feed-forward neural network with tangent activation function that comes with hidden neurons as well as linear output neurons was used. The study applied classical nonlinear least squares algorithm such as LM (Levenberg-Marquardt), to predict potential emissions of selected emerging economies. Building the model on the basis of input variables such as crop production, livestock production, trade imports, trade exports, economic growth, renewable and nonrenewable energy consumption. These variables are considered to affect the ecosystems of high rising economic power states. The main idea is to ensure that emerging economies have a clear understanding of expected future emissions so that appropriate measures can be implemented to mitigate its impact. Data for the analysis were obtained from 1971 to 2013 from World Development Indicators and FAOSTAT database. Results indicate an achievement of training performance at epoch 11 when the value of the MSE (Mean Square Error) is 0.0003345 which indicates that the model errors are less than 0.05. Hence, the study concluded that the applied model is capable of predicting potential carbon dioxide emissions in emerging economies with the greatest precision.

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Prediction of CO₂ Emissions Using an Artificial Neural Network: The Case of the Sugar Industry

Cited by 8 publications

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Ecological Footprint Prediction based on Global Macro Indicators in G-20 Countries using Machine Learning Approaches

Ecological Footprint Prediction based on Global Macro Indicators in G-20 Countries using Machine Learning Approaches

Artificial Neural Network and Its Applications in the Energy Sector – An Overview

Prediction of Potential Carbon Dioxide Emissions of Selected Emerging Economies Using Artificial Neural Network

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Prediction of CO2 Emissions Using an Artificial Neural Network: The Case of the Sugar Industry

Cited by 8 publications

References 0 publications

Ecological Footprint Prediction based on Global Macro Indicators in G-20 Countries using Machine Learning Approaches

Ecological Footprint Prediction based on Global Macro Indicators in G-20 Countries using Machine Learning Approaches

Artificial Neural Network and Its Applications in the Energy Sector – An Overview

Prediction of Potential Carbon Dioxide Emissions of Selected Emerging Economies Using Artificial Neural Network

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Product

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Prediction of CO₂ Emissions Using an Artificial Neural Network: The Case of the Sugar Industry