Prediction of energy content of biomass based on hybrid machine learning ensemble algorithm

In this study, three novel ensemble algorithms, namely, simple averaging, weighted averaging, and meta-learning ensemble algorithms were employed to predict the higher heating value of biomass. These strategies were implemented in two main stages. In the first stage, four heterogeneous standalone mo...

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Main Authors: Usman Alhaji Dodo, Evans Chinemezu Ashigwuike, Jonas Nwachukwu Emechebe, Sani Isah Abba
Format: Article
Language:English
Published: Elsevier 2022-12-01
Series:Energy Nexus
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2772427122001127
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author Usman Alhaji Dodo
Evans Chinemezu Ashigwuike
Jonas Nwachukwu Emechebe
Sani Isah Abba
author_facet Usman Alhaji Dodo
Evans Chinemezu Ashigwuike
Jonas Nwachukwu Emechebe
Sani Isah Abba
author_sort Usman Alhaji Dodo
collection DOAJ
description In this study, three novel ensemble algorithms, namely, simple averaging, weighted averaging, and meta-learning ensemble algorithms were employed to predict the higher heating value of biomass. These strategies were implemented in two main stages. In the first stage, four heterogeneous standalone models: an artificial neural network, a multivariate regression, a support vector regression, and an adaptive neuro-fuzzy inference system (ANFIS) were developed to predict the higher heating value. In the second stage, the outputs of the standalone models were aggregated for ensemble learning implementation. Seven input combinations of the biomass proximate analysis components formed the proposed models’ inputs. In the pre-ensemble phase, the ANFIS model having ash and volatile matter as an input combination presented the most accurate performance based on the Willmott's index of agreement of 0.9741 and the mean square error of 0.0032. The ensemble algorithms demonstrated improvement in the overall prediction performances with the meta-learning ensemble ranked superior for an average error decrease of up to 15% when ash, volatile matter, and fixed carbon served as the model's input combination. The findings of this work provide a robust foundation for the use of ensemble algorithms in the prediction of the biomass higher heating value.
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spelling doaj.art-dae2f3cb2e8844cdb65bd3d8a11822152022-12-22T02:58:49ZengElsevierEnergy Nexus2772-42712022-12-018100157Prediction of energy content of biomass based on hybrid machine learning ensemble algorithmUsman Alhaji Dodo0Evans Chinemezu Ashigwuike1Jonas Nwachukwu Emechebe2Sani Isah Abba3Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Abuja, Nigeria; Department of Electrical and Computer Engineering, Faculty of Engineering, Baze University, Abuja, Nigeria; Corresponding author at: Department of Electrical and Computer Engineering, Faculty of Engineering, Baze University, Abuja, Federal Capital Territory, Nigeria.Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Abuja, NigeriaDepartment of Electrical and Electronics Engineering, Faculty of Engineering, University of Abuja, NigeriaInterdisciplinary Research Centre for Membrane and Water Security, King Fahd University of Petroleum and Minerals Dhahran 31261, Saudi ArabiaIn this study, three novel ensemble algorithms, namely, simple averaging, weighted averaging, and meta-learning ensemble algorithms were employed to predict the higher heating value of biomass. These strategies were implemented in two main stages. In the first stage, four heterogeneous standalone models: an artificial neural network, a multivariate regression, a support vector regression, and an adaptive neuro-fuzzy inference system (ANFIS) were developed to predict the higher heating value. In the second stage, the outputs of the standalone models were aggregated for ensemble learning implementation. Seven input combinations of the biomass proximate analysis components formed the proposed models’ inputs. In the pre-ensemble phase, the ANFIS model having ash and volatile matter as an input combination presented the most accurate performance based on the Willmott's index of agreement of 0.9741 and the mean square error of 0.0032. The ensemble algorithms demonstrated improvement in the overall prediction performances with the meta-learning ensemble ranked superior for an average error decrease of up to 15% when ash, volatile matter, and fixed carbon served as the model's input combination. The findings of this work provide a robust foundation for the use of ensemble algorithms in the prediction of the biomass higher heating value.http://www.sciencedirect.com/science/article/pii/S2772427122001127BiomassEnsemble learningHigher heating valueMachine learningProximate analysis
spellingShingle Usman Alhaji Dodo
Evans Chinemezu Ashigwuike
Jonas Nwachukwu Emechebe
Sani Isah Abba
Prediction of energy content of biomass based on hybrid machine learning ensemble algorithm
Energy Nexus
Biomass
Ensemble learning
Higher heating value
Machine learning
Proximate analysis
title Prediction of energy content of biomass based on hybrid machine learning ensemble algorithm
title_full Prediction of energy content of biomass based on hybrid machine learning ensemble algorithm
title_fullStr Prediction of energy content of biomass based on hybrid machine learning ensemble algorithm
title_full_unstemmed Prediction of energy content of biomass based on hybrid machine learning ensemble algorithm
title_short Prediction of energy content of biomass based on hybrid machine learning ensemble algorithm
title_sort prediction of energy content of biomass based on hybrid machine learning ensemble algorithm
topic Biomass
Ensemble learning
Higher heating value
Machine learning
Proximate analysis
url http://www.sciencedirect.com/science/article/pii/S2772427122001127
work_keys_str_mv AT usmanalhajidodo predictionofenergycontentofbiomassbasedonhybridmachinelearningensemblealgorithm
AT evanschinemezuashigwuike predictionofenergycontentofbiomassbasedonhybridmachinelearningensemblealgorithm
AT jonasnwachukwuemechebe predictionofenergycontentofbiomassbasedonhybridmachinelearningensemblealgorithm
AT saniisahabba predictionofenergycontentofbiomassbasedonhybridmachinelearningensemblealgorithm