Title: Prediction of solar Stirling power generation in smart grid by GA-ANN model

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.

DOI: 10.1504/IJCAT.2017.082860

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: 14 Mar 2017 *

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