Day Type Identification for Algerian Electricity Load using Kohonen Maps

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.

Authors:

• Mohamed Tarek Khadir
• Damien Fay
• Ahmed Boughrira

Keywords:

• Day type identification
• electricity Load
• Kohonenmaps
• load forecasting.

References:

[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).