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In the presented paper by analyzing the curve of the daily electrical network load in Iran over a 10 year period; the effective factors on the daily electricity consumption (including time, environmental and special factors) are studied. Additionally , using the final results from this graphical analysis, a suitable method to train artificial neural networks for short-term forecasting of the time...
In this paper sensibility of Iran network load to the temperature has been studied by the linear regression method. The linear regression analysis of Iranpsilas electrical load recognizes that the load pattern is heavily dependent on temperature, and finds a linear function between the load and the networkpsilas temperature. This study shows that temperature has a great effect on Iranpsilas network...
This paper presents a novel model for short term forecasting of daily electrical load curve in power systems by using of two Kohonen neural networks (KNNs).The proposed model is sensitive to atmospheric factors such as temperature. In addition it is able to forecast normal and abnormal days of year such as holidays, ceremonies, religious and etc, with high accuracy. Ten models are considered for forecasting...
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