Improving the Performance of Short-Term Load Forecast Using a Hybrid Artificial Neural Network and Artificial Bee Colony Algorithm

Tools such as short-term load forecast (STLF) play an ever-important role in the operation and planning of power systems. Improving STLF accuracy could reduce heavy penalties and missed financial opportunities. In this article, a new hybrid forecast method is proposed to improve STLF accuracy. The proposed hybrid STLF method is based on artificial neural network (ANN) and artificial bee colony (ABC) algorithms. The ABC algorithm is used to optimize the learning procedure of ANN. New load modeling is presented based on historical and weather data. Bad data elimination and calendar effects are considered in the STLF procedure. The proposed hybrid STLF method is verified by forecasting the Bushehr province demand. The results proved that the proposed method significantly improved STLF accuracy