Authors: Mohammad Sameti; Mohammad Ali Jokar; Fatemeh Razi Astaraei
Addresses: Department of Building, Civil, and Environmental Engineering, Concordia University, Montreal, Quebec, Canada ' Department of Building, Civil, and Environmental Engineering, Concordia University, Montreal, Quebec, Canada ' Department of Renewable Energies, University of Tehran, Tehran, Iran
Abstract: A model based on the feed-forward Artificial Neural Network (ANN) optimised by the Genetic Algorithm (GA) is developed in order to estimate the power of a solar Stirling heat engine in a smart grid. Genetic Algorithm is used to decide the initial weights of the neural network. The GA-ANN model is applied to predict the power of the solar Stirling heat engine from a data set reported in literature. The performance of the GA-ANN model is compared with numerical data. The results demonstrate the effectiveness of the GA-ANN model.
Keywords: solar power; solar energy; Stirling heat engine; smart grid; artificial neural networks; ANNs; genetic algorithms; power prediction; power estimation; power forecasting.
International Journal of Computer Applications in Technology, 2017 Vol.55 No.2, pp.147 - 157
Received: 02 Dec 2015
Accepted: 22 Feb 2016
Published online: 01 Mar 2017 *