Abbas joodaki - Pak Atieh renewable energy production R&D

As the today’s competitive and industrial world’s economy heavily depends on electrical energy, the electrical energy is not storable and the production more or less than the required amount is followed by losses, planning for the production of electrical energy especially for the peak electrical load is one of the most important electricity generation scheduling operations for the next days, weeks, months and years. In the last two decades many studies have been done on the application of artificial intelligence techniques for load forecasting, among which the artificial neural networks have attracted a lot of attention. The neural network techniques are widely used in load forecasting due to their good capability in nonlinear modeling.Artificial Neural Networks (ANN) can be used in mid-term load forecasting (MTLF) for load distribution applications. The neural network training method because of its success rate and complications caused by providing information has made the researchers to analyze network training process by various methods and in this paper network training is done by COA as one of the new algorithms and its results will be studied in addressing the mentioned problems

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