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Day Type Identification for Algerian Electricity Load using Kohonen Maps

Authors: Mohamed Tarek Khadir, Damien Fay, Ahmed Boughrira


Short term electricity demand forecasts are required by power utilities for efficient operation of the power grid. In a competitive market environment, suppliers and large consumers also require short term forecasts in order to estimate their energy requirements in advance. Electricity demand is influenced (among other things) by the day of the week, the time of year and special periods and/or days such as Ramadhan, all of which must be identified prior to modelling. This identification, known as day-type identification, must be included in the modelling stage either by segmenting the data and modelling each day-type separately or by including the day-type as an input. Day-type identification is the main focus of this paper. A Kohonen map is employed to identify the separate day-types in Algerian data.

Keywords: Load Forecasting, electricity load, Day type identification, Kohonenmaps

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[1] Francisco J. Nogales, Javier Contreras, Antonio J. Conejo and Rosario Espnola, 2002, Forecasting Next-Day Electricity Prices by Time Series Models, IEEE Transactions on Power Systems, Vol. 17(2), pages 342- 348.
[2] Sharif S.S. and Taylor J.H., 2002, Real-Time Load Forecasting by Artificial Neural Networks, IEEE Power Engineering Society Summer Meeting, Vol 1, pp 496-501.
[3] Fay, D., 2004, A strategy for short-term load forecasting in Ireland, Ph.D Thesis, Dept. of Electronic Engineering, Dublin City University, Ireland.
[4] Magali, R., Meireles, G., Paulo, E., Almeida, M. and Sim¨oes, M.G., 2003. A comprehensive review for industrial applicability of artificial neural networks IEEE Transactions on Industrial Eletronics, Vol 50 (3), pp 585-60.
[5] Fay, D., Ringwood, J.V., Condon, M. and Kelly, M. 2003. 24-hour electrical load data - a sequential or partitioned time series? Journal of Neurocomputing, Vol 55, (3-4), pp 469-498.
[6] Hsu, Y.Y. , Yang, C.C., 1991, Design of artificial neural networks for short-term load forecasting Part I: Self-organising feature maps for day type identification, IEE Proceedings-C, 138(5), page 407-413.
[7] Muller, H., Petrisch, G., 1998, Energy and load forecasting by fuzzyneuralnetworks. In: Jurgen, H., Zimmermann, H.J., eds., Proceedings, European Congress on Intelligent Techniques and Soft Computing, Aachen, Germany, September 1998. Aachen: Elite foundation, 1925-1929.
[8] Bretschneider, P., Rauschenbach, T., Wernstedt, J., 1999, Forecast using an adaptive fuzzy classification algorithm for load, 6th European Congress on Intelligent Techniques and Soft Computing, Vol.3, pp 1916- 1919.
[9] Hubele, N.F., Cheng, C.S., 1990, Identification of seasonal short-term forecasting models using statistical decision functions, IEEE Transactions on Power Systems, 5 (1), 40-45.
[10] Srinivasan, D., Tan, S. S., Chang, C. S., Chan, E. K., 1999, Parallel neural network-fuzzy expert system for short-term load forecasting: system implementation and performance evaluation, IEEE Transactions on Power Systems, 14 (3), 1100-1106.
[11] Mastorocotas P.A., Theocharis, J.B., Bakirtzis, A.G., 1999, Fuzzy modelling for short term load forecasting using the orthogonal least squares method, IEEE Transactions on Power Systems, 14 (1), 29-35.
[12] Chen, S.T., Yu, D.C., Moghaddamjo, A.R., 1992, Weather sensitive short-term load forecasting using non-fully connected artificial neural network, IEEE Transactions on Power Systems, 7 (3), 1098-1104.
[13] Lertpalangsunti, N., Chan, C.W., 1998, An architectural framework for the construction of hybrid intelligent forecasting systems: application for electricity demand prediction., Engineering Applications of Artificial Intelligence, 11, 549-565.
[14] Kohonen , T., 1990, The self-organising map, Proceedings IEEE, 78 (9).