| dc.creator |
Kichonge, Baraka |
|
| dc.creator |
Tesha, Thomas |
|
| dc.creator |
Mkilaha, Iddi |
|
| dc.creator |
John, Geoffrey |
|
| dc.date |
2016-07-12T09:27:24Z |
|
| dc.date |
2016-07-12T09:27:24Z |
|
| dc.date |
2014-12 |
|
| dc.date.accessioned |
2018-03-27T08:37:57Z |
|
| dc.date.available |
2018-03-27T08:37:57Z |
|
| dc.identifier |
Kichonge, B., Tesha, T., Mkilaha, I.S. and John, G.R., 2014. Analysis of Tanzanian Energy Demand using Artificial Neural Network and Multiple Linear Regression. International Journal of Computer Applications, 108(2). |
|
| dc.identifier |
http://hdl.handle.net/20.500.11810/3101 |
|
| dc.identifier |
10.5120/18882-0161 |
|
| dc.identifier.uri |
http://hdl.handle.net/20.500.11810/3101 |
|
| dc.description |
Full text can be accessed at
http://search.proquest.com/openview/9bc3835fc75bbc0538b14f38badc12d7/1?pq-origsite=gscholar |
|
| dc.description |
Analysis of energy demand is of a vital concern to energy
systems analysts and planners in any nation. This paper
present artificial neural network-multilayer perceptron (ANNMLP)
and multiple linear regression (MLR) techniques for the
analysis of energy demand in Tanzania. The techniques were
employed to analyze the influence of economic, energy and
environment indicators models in predicting the energy
demand in Tanzania. Statistical performance indices were
used to evaluate the prediction ability of economic, energy
and environment indicators models using ANN-MLP and
MLR techniques. Predicted responses values of ANN-MLP
and MLR techniques were then compared to determine their
closeness with actual data values for determining the best
performing technique. The results from ANN-MLP and MLR
techniques showed the best model for predicting the energy
demand in Tanzania were from energy indicators as opposed
to economic and environmental indicators. The ANN-MLP
prediction values had a correlation coefficient (CC) of 0.9995
and mean absolute percentage error (MAPE) of 0.67%
outperforming the MLR technique whose CC and MAPE
values were 0.9993 and 0.83% respectively. ANN-MLP
technique graphical presentation of actual against predicted
values showed close relationship between actual and predicted
values as opposed to the MLR technique whose predicted
values deviated much from actual values. Analysis of results
from both techniques conclude that ANN-MLP outperform
MLR technique in predicting energy demand in Tanzania. |
|
| dc.language |
en |
|
| dc.subject |
ANN |
|
| dc.subject |
Absolute error |
|
| dc.subject |
Energy demand prediction |
|
| dc.subject |
Multi linear regression |
|
| dc.title |
Analysis of Tanzanian Energy Demand Using Artificial Neural Network and Multiple Linear Regression |
|
| dc.type |
Journal Article |
|