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How to cite this paper
APA: Abdulsalam, K & Babatunde, O. (2019). Electrical energy demand forecasting model using artificial neural network: A case study of Lagos State Nigeria. International Journal of Data and Network Science, 3(4), 305-322.
Chicago/Turabian: Abdulsalam, K & Babatunde, O. 2019. "Electrical energy demand forecasting model using artificial neural network: A case study of Lagos State Nigeria." International Journal of Data and Network Science 3, no. 4 (2019): 305-322.
AMA: Abdulsalam, K & Babatunde, O. Electrical energy demand forecasting model using artificial neural network: A case study of Lagos State Nigeria. International Journal of Data and Network Science. 2019;3(4):305-322.
References
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Cichocki, A., & Amari, S. I. (2002). Adaptive blind signal and image processing: learning algorithms and applications (Vol. 1). John Wiley & Sons.
Dy, J. G. (2008). Unsupervised Feature Selection. In J. G. Dy, L. Huan, & M. Hiroshi (Eds.), Computa-tional Methods of Feature Selection. US: Taylor and Francis Group.
Fausett, L. (1994). Fundamentals of Neural Networks: Architectures. Algorithms and Application. NJ: Englewood Cliffs.
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Haykin, S. (1996). Adaptive Filter Theory. 3rd ed. London: Prentice Hall International.
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Jain, A. K., Mao, J., & Mohiuddin, K. M. (1996). Artificial neural networks: A tutorial. Computer, (3), 31-44.
Jose, P. P., & Abraham, H. O. (1992). Physics of Climate. NY: Springer Verlag.
Kevin, S. (2001). Applying Neural Networks - a Practical Guide (3rd ed.). England: Academic Press.
Konstantinos, I. D. (2002). Neural Networks and Principal Component Analysis. In I. D. Konstantinos, & Y. H. Hu (Ed.), Handbook of Neural Network Signal Processing (pp. 211-248). USA: CRC Press.
Lappas, G. (2007). Estimating the Size of Neural Network from the Number of Available Training Da-ta. In M. d. Joaquim, A. A. Luís, D. Włodzisław, & P. M. Danilo (Ed.), ANN ICANN, 17th Interna-tional Conference (pp. 68-77). Portugal: Springer Berlin Heidelberg.
Mandic, D. P., & Chambers, J. A. (2001). Recurrent Neural Networks for Prediction. England: John Wiley & sons.
Murata, N., Yoshizawa, S., & Amari, S. I. (1994). Network information criterion-determining the num-ber of hidden units for an artificial neural network model. IEEE transactions on neural net-works, 5(6), 865-872.
Okoye, J.K. (2007). Background Study on Water and Energy Issues in Nigeria. The National Consulta-tive Conference on Dams and Development. Nigeria.
Robert, S. (2006). Dynamic Population Models. Netherlands: Springer .
Rodvold, D. M. (1999). A software development process model for artificial neural networks in critical applications. In IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No. 99CH36339) (Vol. 5, pp. 3317-3322). IEEE.
Tsoi, A. C., & Back, A. (1997). Discrete time recurrent neural network architectures: A unifying re-view. Neurocomputing, 15(3-4), 183-223.
Zaman, S., & Karray, F. (2009, January). Features selection using fuzzy ESVDF for data dimensionali-ty reduction. In 2009 International Conference on Computer Engineering and Technology (Vol. 1, pp. 81-87). IEEE.
Beale, M. H., Hagan, M. T., & Demuth, H. B. (2010). Neural Network Toolbox User's Guide. The MathWorks. Inc., Natick, MA.
Bhattacharyya, S. C., & Timilsina, G. R. (2009). Energy demand models for policy formulation: a com-parative study of energy demand models. The World Bank.
Cichocki, A., & Amari, S. I. (2002). Adaptive blind signal and image processing: learning algorithms and applications (Vol. 1). John Wiley & Sons.
Dy, J. G. (2008). Unsupervised Feature Selection. In J. G. Dy, L. Huan, & M. Hiroshi (Eds.), Computa-tional Methods of Feature Selection. US: Taylor and Francis Group.
Fausett, L. (1994). Fundamentals of Neural Networks: Architectures. Algorithms and Application. NJ: Englewood Cliffs.
Guyon, I., Gunn, S., Nikravesh, M., & Zadeh, L. A. (Eds.). (2008). Feature extraction: foundations and applications (Vol. 207). Springer.
Haykin, S. (1996). Adaptive Filter Theory. 3rd ed. London: Prentice Hall International.
Haykin, S. (1999). Neural Networks: A Comprehensive Foundation. 2nd ed., New Jersey: Prentice Hall Inc.
Jain, A. K., Mao, J., & Mohiuddin, K. M. (1996). Artificial neural networks: A tutorial. Computer, (3), 31-44.
Jose, P. P., & Abraham, H. O. (1992). Physics of Climate. NY: Springer Verlag.
Kevin, S. (2001). Applying Neural Networks - a Practical Guide (3rd ed.). England: Academic Press.
Konstantinos, I. D. (2002). Neural Networks and Principal Component Analysis. In I. D. Konstantinos, & Y. H. Hu (Ed.), Handbook of Neural Network Signal Processing (pp. 211-248). USA: CRC Press.
Lappas, G. (2007). Estimating the Size of Neural Network from the Number of Available Training Da-ta. In M. d. Joaquim, A. A. Luís, D. Włodzisław, & P. M. Danilo (Ed.), ANN ICANN, 17th Interna-tional Conference (pp. 68-77). Portugal: Springer Berlin Heidelberg.
Mandic, D. P., & Chambers, J. A. (2001). Recurrent Neural Networks for Prediction. England: John Wiley & sons.
Murata, N., Yoshizawa, S., & Amari, S. I. (1994). Network information criterion-determining the num-ber of hidden units for an artificial neural network model. IEEE transactions on neural net-works, 5(6), 865-872.
Okoye, J.K. (2007). Background Study on Water and Energy Issues in Nigeria. The National Consulta-tive Conference on Dams and Development. Nigeria.
Robert, S. (2006). Dynamic Population Models. Netherlands: Springer .
Rodvold, D. M. (1999). A software development process model for artificial neural networks in critical applications. In IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No. 99CH36339) (Vol. 5, pp. 3317-3322). IEEE.
Tsoi, A. C., & Back, A. (1997). Discrete time recurrent neural network architectures: A unifying re-view. Neurocomputing, 15(3-4), 183-223.
Zaman, S., & Karray, F. (2009, January). Features selection using fuzzy ESVDF for data dimensionali-ty reduction. In 2009 International Conference on Computer Engineering and Technology (Vol. 1, pp. 81-87). IEEE.