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One day ahead load forecasting for electricity market of Iran by ANN

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3 Author(s)
Azadeh, A. ; Dept. of Ind. Eng., Univ. of Tehran, Tehran ; Ghadrei, S.F. ; Nokhandan, B.P.

One of the basic requirements for power systems is accurate short-term load forecasting (STLF). In this study, the application of artificial neural networks is explored for designing of short-term load forecasting systems for electricity market of Iran. In this paper, two seasonal artificial neural networks (ANNs) are designed and compared; so that model 2 (hourly load forecasting model) is partitioning of model 1 (daily load forecasting model). Our study based on feed-forward back propagation is trained and tested using three years (2003-2005) data. At the end, extensive data sets test the results; and good agreement is founded between actual data and NN results. Results show that daily forecasting model is better than the hourly one.

Published in:

Power Engineering, Energy and Electrical Drives, 2009. POWERENG '09. International Conference on

Date of Conference:

18-20 March 2009