Short Term Load Forecasting Based Artificial Neural Network | ||
| Iraqi Journal for Electrical And Electronic Engineering | ||
| Article 1, Volume 10, Issue 1, June 2014, Pages 42-47 PDF (0 K) | ||
| DOI: 10.33762/eeej.2014.94476 | ||
| Author | ||
| Adel M. Dakhil | ||
| Abstract | ||
| Present study develops short term electric load forecasting using neural network; based on historical series of power demand the neural network chosen for this network is feed forward network, this neural network has five input variables ( hour of the day, the day of the week, the load for the previous hour, the load of the pervious day, the load for the previous week). Short term load forecast is very important due to accurate for power system operation and analysis system security among other mandatory function. The trained artificial neural network shows good accuracy and robust in forecasting future load demands for the daily operation, mean absolute percentage error (MAPE) was calculated and it is maximum value is 0.75% in load forecasting on Monday | ||
| Keywords | ||
| Actual load; Feed forward; Load forecasting; neural network; predicted load; Short term load forecasting | ||
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