Feature selection and optimization of artificial neural network for short term load forecasting

The electric load influenced by different factors such as meteorological, economics, and casual factors according to demographic location, time, and the human behavior. This classification simplified studying the correlation between those factors. It is important to select the right set of parameters that affect the forecasting. Selecting irrelevant parameters will require additional computation time and may not improve the forecasting accuracy. This work introduces the effect of electrical load factors in short term load forecasting. In this work, several factors (temperature, due temperature, wind, and humidity) are applied to ANN to understand its impact on electric load forecasting of Northern Cairo. From the experimental results, we show that MAPE, RMSE, and MAE are decreased by more than half after using the proposed model.