Prediction of Tanzanian energy demand using support vector machine for regression (SVR)

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dc.contributor.authorKichonge, Baraka
dc.contributor.authorJohn, Geoffrey R.
dc.contributor.authorTesha, Thomas
dc.contributor.authorMkilaha, Iddi S.N.
dc.date.accessioned2023-10-12T12:21:48Z
dc.date.available2023-10-12T12:21:48Z
dc.date.issued2015
dc.descriptionAbstract. Full text article. Also available at https://tinyurl.com/bdzej62ten_US
dc.description.abstractThis study discusses the influences of economic, energy and environment indicators in the prediction of energy demand for Tanzania applying support vector machine for regression (SVR). Economic, energy and environment indicators were applied to formulate models based on time series data. The experimental results showed the supremacy of the polynomial-SVR kernel function and the energy indicators model in providing the transformation, which achieved more accurate prediction values. The energy indicators model had a correlation coefficient (CC) of 0.999 as equated to 0.9975 and 0.9952 with PUKF-SVR kernels for economic and environment indicators model. The energy indicators model closeness of predicted values as compared to actual values was the best as compared to economic and environment indicators models. Furthermore, root mean squared error (RMSE), mean absolute error (MAE), root relative squared error (RRSE) and relative absolute error (RAE) of energy indicators model were the lowest. Long-run sustainable development of the energy sector can be achieved with the use of SVR-algorithm as prediction tool of future energy demand.en_US
dc.identifier.citationKichonge, B., John, G. R., Tesha, T., & Mkilaha, I. S. N. (2015). Prediction of tanzanian energy demand using support vector machine for regression (SVR). International Journal of Computer Applications, 109(3), 34-39.en_US
dc.identifier.otherURL: https://tinyurl.com/bdzej62t
dc.identifier.urihttp://hdl.handle.net/20.500.12661/4136
dc.language.isoenen_US
dc.publisherTaylor and Francis Ltden_US
dc.subjectEnergy demanden_US
dc.subjectEnergy demand indicatorsen_US
dc.subjectEnergy demand predictionen_US
dc.subjectSupport vector machine for regressionen_US
dc.subjectTanzanian energy demanden_US
dc.subjectSVRen_US
dc.subjectSVR-algorithmen_US
dc.subjectEnergy indicators modelen_US
dc.titlePrediction of Tanzanian energy demand using support vector machine for regression (SVR)en_US
dc.typeArticleen_US
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