We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

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

by Baraka Kichonge, Thomas Tesha, Iddi S.n. Mkilaha, Geoffrey R John
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 108 - Number 2
Year of Publication: 2014
Authors: Baraka Kichonge, Thomas Tesha, Iddi S.n. Mkilaha, Geoffrey R John
10.5120/18882-0161

Baraka Kichonge, Thomas Tesha, Iddi S.n. Mkilaha, Geoffrey R John . Analysis of Tanzanian Energy Demand using Artificial Neural Network and Multiple Linear Regression. International Journal of Computer Applications. 108, 2 ( December 2014), 13-20. DOI=10.5120/18882-0161

@article{ 10.5120/18882-0161,
author = { Baraka Kichonge, Thomas Tesha, Iddi S.n. Mkilaha, Geoffrey R John },
title = { Analysis of Tanzanian Energy Demand using Artificial Neural Network and Multiple Linear Regression },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 108 },
number = { 2 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 13-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume108/number2/18882-0161/ },
doi = { 10.5120/18882-0161 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:41:56.472013+05:30
%A Baraka Kichonge
%A Thomas Tesha
%A Iddi S.n. Mkilaha
%A Geoffrey R John
%T Analysis of Tanzanian Energy Demand using Artificial Neural Network and Multiple Linear Regression
%J International Journal of Computer Applications
%@ 0975-8887
%V 108
%N 2
%P 13-20
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

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 (ANN-MLP) 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.

References
  1. Reister, D. B. (1987) The link between energy and GDP in developing countries, Energy, 12, 427-433.
  2. Apergis, N. and Payne, J. E. (2009) Energy consumption and economic growth: Evidence from the Commonwealth of Independent States, Energy Economics, 31, 641-647.
  3. Soytas, U. and Sari, R. (2003) Energy consumption and GDP: causality relationship in G-7 countries and emerging markets, Energy economics, 25, 33-37.
  4. Mozumder, P. and Marathe, A. (2007) Causality relationship between electricity consumption and GDP in Bangladesh, Energy policy, 35, 395-402.
  5. Odhiambo, N. M. (2009) Energy consumption and economic growth nexus in Tanzania: An ARDL bounds testing approach, Energy Policy, 37, 617-622.
  6. Ebohon, O. J. (1996) Energy, economic growth and causality in developing countries: A case study of Tanzania and Nigeria, Energy policy, 24, 447-453.
  7. Vera, I. and Langlois, L. (2007) Energy indicators for sustainable development, Energy, 32, 875-882.
  8. Mellit, A. , Kalogirou, S. A. , Hontoria, L. and Shaari, S. (2009) Artificial intelligence techniques for sizing photovoltaic systems: A review, Renewable and Sustainable Energy Reviews, 13, 406-419.
  9. Mubiru, J. and Banda, E. (2008) Estimation of monthly average daily global solar irradiation using artificial neural networks, Solar Energy, 82, 181-187.
  10. Sözen, A. , Arcakl?o?lu, E. , Özalp, M. and Ça?lar, N. (2005) Forecasting based on neural network approach of solar potential in Turkey, Renewable Energy, 30, 1075-1090.
  11. Azadeh, A. , Maghsoudi, A. and Sohrabkhani, S. (2009) An integrated artificial neural networks approach for predicting global radiation, Energy Conversion and Management, 50, 1497-1505.
  12. Morel, N. , Bauer, M. , El-Khoury, M. and Krauss, J. (2001) Neurobat, a predictive and adaptive heating control system using artificial neural networks, International Journal of Solar Energy, 21, 161-201.
  13. Kalogirou, S. A. (2003) Artificial intelligence for the modeling and control of combustion processes: a review, Progress in Energy and Combustion Science, 29, 515-566.
  14. Fadare, D. (2010) The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria, Applied Energy, 87, 934-942.
  15. Jain, A. K. , Mao, J. and Mohiuddin, K. (1996) Artificial neural networks: A tutorial, Computer, 29, 31-44.
  16. Park, D. C. , El-Sharkawi, M. , Marks, R. , Atlas, L. and Damborg, M. (1991) Electric load forecasting using an artificial neural network, Power Systems, IEEE Transactions on, 6, 442-449.
  17. Svozil, D. , Kvasnicka, V. and Pospichal, J. Í. (1997) Introduction to multi-layer feed-forward neural networks, Chemometrics and intelligent laboratory systems, 39, 43-62.
  18. Mcculloch, W. S. and Pitts, W. (1943) A logical calculus of the ideas immanent in nervous activity, The bulletin of mathematical biophysics, 5, 115-133.
  19. Rosenblatt, F. (1961) Principles of neurodynamics. perceptrons and the theory of brain mechanisms (DTIC Document).
  20. Bishop, C. M. (2006) Pattern recognition and machine learning (springer New York).
  21. Campbell, M. J. (2001) Multiple linear regression, Statistics at Square Two: Understanding Modern Statistical Applications in Medicine, Second Edition, 10-31.
  22. Tranmer, M. and Elliot, M. (2008) Multiple linear regression, The Cathie Marsh Centre for Census and Survey Research (CCSR).
  23. Sharma, D. P. , Chandramohanan Nair, P. and Balasubramanian, R. (2002) Demand for commercial energy in the state of Kerala, India: an econometric analysis with medium-range projections, Energy policy, 30, 781-791.
  24. Bianco, V. , Manca, O. and Nardini, S. (2009) Electricity consumption forecasting in Italy using linear regression models, Energy, 34, 1413-1421.
  25. Yee, Y. Y. (1998) Climate and residential electricity consumption in Hong Kong, Energy, 23, 17-20.
  26. Hall, M. , Frank, E. , Holmes, G. et al. (2009) The WEKA data mining software: an update, ACM SIGKDD explorations newsletter, 11, 10-18.
  27. Witten, I. H. , Frank, E. , Trigg, L. E. et al. (1999) Weka: Practical machine learning tools and techniques with Java implementations.
  28. Garner, S. R. (1995) Weka: The waikato environment for knowledge analysis, Paper presented at the Proceedings of the New Zealand computer science research students conference.
  29. Azadeh, A. , Ghaderi, S. , Tarverdian, S. and Saberi, M. (2007) Integration of artificial neural networks and genetic algorithm to predict electrical energy consumption, Applied Mathematics and Computation, 186, 1731-1741.
  30. Chattefuee, S. and Hadi, A. S. (2006) Regression Analysis by Example (New Jersey, A John Wiley & Sons, Inc. , Publication ).
  31. Lee, R. J. and Nicewander, W. A. (1988) Thirteen ways to look at the correlation coefficient, The American Statistician, 42, 59-66.
  32. Armstrong, J. S. and Collopy, F. (1992) Error measures for generalizing about forecasting methods: Empirical comparisons, International journal of forecasting, 8, 69-80.
  33. Makridakis, S. and Hibon, M. (1995) Evaluating accuracy (or error) measures (INSEAD).
Index Terms

Computer Science
Information Sciences

Keywords

ANN absolute error energy demand prediction multi linear regression.

Powered by PhDFocusTM