Analysis of Tanzanian Energy Demand Using Artificial Neural Network and Multiple Linear Regression

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dc.creatorKichonge, Baraka
dc.creatorTesha, Thomas
dc.creatorMkilaha, Iddi
dc.creatorJohn, Geoffrey
dc.date2016-07-12T09:27:24Z
dc.date2016-07-12T09:27:24Z
dc.date2014-12
dc.date.accessioned2018-03-27T08:37:57Z
dc.date.available2018-03-27T08:37:57Z
dc.descriptionFull text can be accessed at http://search.proquest.com/openview/9bc3835fc75bbc0538b14f38badc12d7/1?pq-origsite=gscholar
dc.descriptionAnalysis 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.identifierKichonge, 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.identifierhttp://hdl.handle.net/20.500.11810/3101
dc.identifier10.5120/18882-0161
dc.identifier.urihttp://hdl.handle.net/20.500.11810/3101
dc.languageen
dc.subjectANN
dc.subjectAbsolute error
dc.subjectEnergy demand prediction
dc.subjectMulti linear regression
dc.titleAnalysis of Tanzanian Energy Demand Using Artificial Neural Network and Multiple Linear Regression
dc.typeJournal Article

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Department of Mechanical and Industrial Engineering