Half-hour global solar radiation forecasting based on static and dynamic multivariate neural networks
Joint Authors
Jalal, Muhammad Ali
Zeroual, Abd al-Wahhab
Chabaa, Samirah
Source
Journal of Engineering Research
Issue
Vol. 9, Issue 2 (30 Jun. 2021), pp.203-217, 15 p.
Publisher
Kuwait University Academic Publication Council
Publication Date
2021-06-30
Country of Publication
Kuwait
No. of Pages
15
Main Subjects
Telecommunications Engineering
Abstract EN
Precise global solar radiation (GSR) measurements in a given location are very essential for designing and supervising solar energy systems.
in the case of rarity or absence of these measurements, it is important to have a theoretical or empirical model to compute the GSR values.
therefore, the main goal of this work is to offer, to designers and engineers of solar energy systems, an appropriate and accurate way to predict the half-hour global solar radiation (HHGSR) time series from some available meteorological parameters (relative humidity, air temperature, wind speed, precipitation, and acquisition time vector in half-hour scale).
for that purpose, two intelligent models are developed : the first one is a multivariate dynamic neural network with feedback connection, and the second is a multivariate static neural network.
the database used to build these models was recorded in Agdal's meteorological station in Marrakesh, Morocco, during the years of 2013 and 2014, and it was divided into two subsets.
the first subset is used for training and validating the models, and the second subset is used for testing the efficiency and the robustness of the developed models.
the obtained results, in terms of the statistical performance indicators, demonstrate the efficiency of the developed forecasting models to accurately predict the HHGSR parameter in the city of Marrakesh, Morocco.
American Psychological Association (APA)
Jalal, Muhammad Ali& Chabaa, Samirah& Zeroual, Abd al-Wahhab. 2021. Half-hour global solar radiation forecasting based on static and dynamic multivariate neural networks. Journal of Engineering Research،Vol. 9, no. 2, pp.203-217.
https://search.emarefa.net/detail/BIM-1494848
Modern Language Association (MLA)
Jalal, Muhammad Ali…[et al.]. Half-hour global solar radiation forecasting based on static and dynamic multivariate neural networks. Journal of Engineering Research Vol. 9, no. 2 (Jun. 2021), pp.203-217.
https://search.emarefa.net/detail/BIM-1494848
American Medical Association (AMA)
Jalal, Muhammad Ali& Chabaa, Samirah& Zeroual, Abd al-Wahhab. Half-hour global solar radiation forecasting based on static and dynamic multivariate neural networks. Journal of Engineering Research. 2021. Vol. 9, no. 2, pp.203-217.
https://search.emarefa.net/detail/BIM-1494848
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 216-217
Record ID
BIM-1494848