PREDICTION INTELLIGENT SYSTEM IN THE FIELD OF RENEWABLE ENERGIES THROUGH NEURAL NETWORKS

Ion Lungu (), Adela Bâra (), George Carutasu (), Alexandru Pîrjan, () and Simona-Vasilica Oprea ()
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Adela Bâra: The Bucharest Academy of Economic Studies
Alexandru Pîrjan,: The Romanian-American University
Simona-Vasilica Oprea: The Bucharest Academy of Economic Studies

ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, 2016, vol. 50, issue 1, 85-102

Abstract: In this paper, we have developed a series of neural networks in order to design a decision support system for predicting, analysing and monitoring the performance indicators in the field of renewable energies in Romania. We have first analysed a series of comparative aspects regarding the algorithms used for developing the neural networks: the Levenberg-Marquardt, the Bayesian Regularization and the Scaled Conjugate Gradient algorithms. Then, we have developed, trained, validated and tested several neural networks based on the above-mentioned algorithms, using the Neural Network Toolbox from the development environment MatlabR2015a. Thus, we have obtained a solution that forecasts the total active energy export and the total active power, when knowing the solar irradiation, the ambient temperature, the humidity, the wind direction and the wind speed.

Keywords: Neural Networks; algorithms; renewable energy; solar power plant; Decision Support System. (search for similar items in EconPapers)
JEL-codes: C01 C15 C53 L86 O13 Q42 Q47 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (8)

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