Full text can be accessed at
teseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.695.2527&rep=rep1&type=pdf
This 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.