Time Series Forecasting with Artificial Neural Network Trained by Imperialist Competitive Algorithm: A Case Study of Renewable Energy Production and Consumption
محورهای موضوعی : انرژی های تجدیدپذیر و هوش مصنوعیMohammad Amirkhan 1 , Salman Amirkhan 2 , Mohammad Reza Aloustani 3
1 - Department of Industrial Engineering, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran
2 - Department of Electrical Engineering, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran
3 - Department of Industrial Engineering, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran
کلید واژه: Forecasting, Time Series, Artificial Neural Network, Imperialist Competitive Algorithm, Renewable Energy Production and Consumption,
چکیده مقاله :
The use of renewable energy sources is very valuable as an important aspect of the sustainability and progress of societies in the future. In recent times, the importance of developing the use of renewable energy and replacing fossil energy with this type of energy is not hidden from any country. In recent times, the importance of developing the use of renewable energy and replacing fossil energy with this type of energy is not hidden from any country, and therefore, the need for powerful models to forecast the amount of production and consumption of this energy is very necessary. The application of evolutionary neural network as a powerful forecasting technique has been gaining more and more attention in recent researches. In this paper, an efficient method based on the artificial neural network (ANN) has been presented to forecast the renewable energy production and consumption. To enhance the efficiency of the network, an evolutionary algorithm called imperialist competitive algorithm (ICA) has been applied to optimize the network weights. The performance of the ANN-ICA is compared with ANN on real data of the aforementioned case study and the results demonstrate the effectiveness of the ANN-ICA.
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