Analysis of Tanzanian Biomass Consumption Using Artificial Neural Network

Thomas Tesha and Baraka Kic

Abstract

The growing biomass consumption in developing countries context is being driven by a mixture of concerns over energy security, sustainable development and the climate change mitigation. The development of the comprehensive, sustainable and efficient biomass energy sector policies, strategies and investments requires proper biomass utilization and planning which in fact has not yet received the attention it deserves in the developing countries policies. This paper aims are twofold; one being to demonstrate the practicability of the application of artificial neural network multilayer perceptron (ANN-MLP) in the analysis of the biomass energy consumption and two, to identify the demographic and economic indicators which works better in the analysis and prediction of biomass consumption in Tanzania. Three models made up of Tanzania rural, Tanzania urban and Tanzania population with the addition of economic indicators were formulated for the analysis. The ANN-MLP has shown promising results with the statistical correlation coefficient of 0.9972 indicating that it can be used for practical analysis and prediction of biomass energy consumption. Furthermore the results show the use of Tanzania population model in the analysis and prediction of biomass consumption gives better results in comparison to the Tanzania rural and Tanzania urban population models individually.