Property-based biomass feedstock grading using k-Nearest Neighbour technique

Obafemi O. Olatunji, Stephen Akinlabi, Nkosinathi Madushele and Paul A. Adedeji

Energy, 2020, vol. 190, issue C

Abstract: Energy generation from biomass requires a nexus of different sources irrespective of origin. A detailed and scientific understanding of the class to which a biomass resource belongs is therefore highly essential for energy generation. An intelligent classification of biomass resources based on properties offers a high prospect in analytical, operational and strategic decision-making. This study proposes the k-Nearest Neighbour (k-NN) classification model to classify biomass based on their properties. The study scientifically classified 214 biomass dataset obtained from several articles published in reputable journals. Four different values of k (k=1,2,3,4) were experimented for various self normalizing distance functions and their results compared for effectiveness and efficiency in order to determine the optimal model. The k–NN model based on Mahalanobis distance function revealed a great accuracy at k=3 with Root Mean Squared Error (RMSE), Accuracy, Error, Sensitivity, Specificity, False positive rate, Kappa statistics and Computation time (in seconds) of 1.42, 0.703, 0.297, 0.580, 0.953, 0.047, 0.622, and 4.7 respectively. The authors concluded that k–NN based classification model is feasible and reliable for biomass classification. The implementation of this classification models shows that k–NN can serve as a handy tool for biomass resources classification irrespective of the sources and origins.

Keywords: Biomass classification; Energy; k-NN classifier; Mahalanobis distance (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544219320419
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:190:y:2020:i:c:s0360544219320419

DOI: 10.1016/j.energy.2019.116346

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().