Medium Term Load Forecasting Using Statistical Feature Self Organizing Maps (SOM)
Keywords:
Artificial Neural Network, Load Forecasting, Medium Term, Self-Organizing Maps,Abstract
Load forecasting is an essential tool for power system activity and planning. With the increase in development and the expansion of power system, it is important for the electrical utility to make a decision in ensuring that there would be enough supply of electricity to deal with the increasing demand. This research presents the Medium Term Load Forecasting using the artificial neural networks: Kohonen’s Self-organizing Maps. The main purpose of this paper was to understand the ability of Self-Organizing Maps in forecasting the load demand and to train and test via SelfOrganizing Maps method using the selected features. Using data provided by the Global Energy Forecasting Competition (GEFCom2012), this paper focused on the missing data from the year 2005 and 2006 for the load forecasting. The loaded data were trained, tested, and forecasted using SOM Toolbox in MATLAB software. The accuracy of the forecasted data was determined by calculating the error of each forecasted data by comparing them with the actual data. Then, the Mean Absolute Percentage Error was computed to determine the accuracy of the results.References
I. A. Samuel, F. C. F, A. A. A, and A. A. Awelewa, “Medium-Term Load Forecasting Of Covenant University Using The Regression Analysis Methods,” vol. 4, no. 4, pp. 10–17, 2014.
N. Amjady and F. Keynia, “Mid-term load forecasting of power systems by a new prediction method,” vol. 49, pp. 2678–2687, 2008.
O. A. S. Carpinteiro and A. P. Alves da Silva, “A hierarchical mboxself-organizing map model in short-term load forecasting,” Eng. Appl. Neural Networks. Proc. 5th Int. Conf. Eng. Appl. Neural Networks, pp. 75–80, 1999.
E. A. Feinberg and D. Genethliou, “Chapter 12 LOAD FORECASTING,” in Applied Mathematics for Restructured Electric Power Systems: Optimization, Control, and Computational Intelligence, Springer, Boston, MA, 2005, pp. 269–285.
E. Gonzalez-Romera, M. A. Jaramillo-Moran, and D. Carmona, Monthly Electric Energy Demand Forecasting Based on Trend Extraction, vol. 21. 2006.
N. Abu-shikhah, F. Elkarmi, and O. M. Aloquili, “Medium-Term Electric Load Forecasting Using Multivariable Linear and Non-Linear Regression,” Smart Grid Renew. Energy, vol. 2, no. May, pp. 126– 135, 2011.
M. Martín-Merino and J. Román, “Electricity Load Forecasting Using Self Organizing Maps BT - Artificial Neural Networks – ICANN 2006,” 2006, pp. 709–716.
G. P. Papaioannou, C. Dikaiakos, A. Dramountanis, and P. G. Papaioannou, “Analysis and Modeling for Short- to Medium-Term Load Forecasting Using a Hybrid Manifold Learning Classical Statistical Models ( SARIMAX , Exponential,” 2016.
A. D. Papalexopoulos, S. Hao, and T.-M. Peng, “An implementation of a neural network based load forecasting model for the EMS,” IEEE Trans. Power Syst., vol. 9, no. 4, pp. 1956–1962, 1994.
A. Mohan, “Mid Term Electrical Load Forecasting For State of Himachal Pradesh Using Different Weather Conditions via ANN Model,” vol. 1, no. 2, pp. 60–63, 2013.
G. Zhang, B. E. Patuwo, and M. Y. Hu, “Forecasting with artificial neural networks : The state of the art,” vol. 14, pp. 35–62, 1998.
M. López, S. Valero, C. Senabre, J. Aparicio, and A. Gabaldon, “Application of SOM neural networks to short-term load forecasting: The Spanish electricity market case study,” Electr. Power Syst. Res., vol. 91, pp. 18–27, 2012.
T. Kohonen, Self-Organizing Maps, 2nd ed. Springer-Verlag Berlin Heidelberg, 2001.
V. Chaudhary, R. S. Bhatia, and A. K. Ahlawat, “A novel SelfOrganizing Map (SOM) learning algorithm with nearest and farthest neurons,” Alexandria Eng. J., vol. 53, no. 4, pp. 827–831, 2014.
S. M. Guthikonda, “Kohonen Self-Organizing Maps,” no. December, 2005.
J. Vesanto, J. Himberg, E. Alhoniemi, and J. Parhankangas, “SOM Toolbox for Matlab 5,” Tech. Rep. A57, vol. 2, no. 0, p. 59, 2000.
A. A. Akinduko, E. M. Mirkes, and A. N. Gorban, “SOM: Stochastic initialization versus principal components,” Inf. Sci. (Ny)., vol. 364– 365, pp. 213–221, 2016.
Z. H. Bohari, H. S. Azemy, M. N. Mohd Nasir, F. Baharom, M. F. Sulaima, and M. Jali, Reliable short term load forecasting using self organizing map (SOM) in deregulated electricity market, vol. 79. 2015.
A.Ultsch and H. P. Siemon, “Kohonen’s SeIf Organizing Feature Maps for Exploratory Data Analysis,” in INNC’90, 1990, pp. 305– 308.
S.-L. Shieh and I.-E. Liao, “A new approach for data clustering and visualization using self-organizing maps,” Expert Syst. Appl., vol. 39, no. 15, pp. 11924–11933, 2012.
T. Hong, P. Pinson, and S. Fan, “Global energy forecasting competition 2012,” Int. J. Forecast., vol. 30, no. 2, pp. 357–363, 2014.
Downloads
Published
How to Cite
Issue
Section
License
TRANSFER OF COPYRIGHT AGREEMENT
The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):
- All proprietary right other than copyright, such as patent rights
- The right to make further copies of all or part of the published article for my use in classroom teaching
- The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
- The right to make copies of the published work for internal distribution within the institution that employs me
I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.