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Most influential variables for solar radiation forecasting using artificial neural networks

Author: Bader M Alluhaidah; Mohamed E El-Hawary; Dalhousie University. Department of Electrical & Computer Engineering,
Publisher: Halifax, NS : Dalhousie University, 2014. ©2014
Dissertation: M.A. Sc. Dalhousie University 2014
Edition/Format:   Thesis/dissertation : Document : Thesis/dissertation : eBook   Computer File : English
Summary:
ABSTRACT: Decaying fossil fuel resources, international relation complexities, and the risks associated with nuclear power have led to an increased demand for alternative energy sources. Renewable energy sources offer adequate solutions to these challenges. Forecasting of solar energy has also increased over the past decade due to its use in photovoltaic (PV) system design, load balance in hybrid systems, and  Read more...
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Genre/Form: Electronic thesis or dissertation
Academic theses
Material Type: Document, Thesis/dissertation, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Bader M Alluhaidah; Mohamed E El-Hawary; Dalhousie University. Department of Electrical & Computer Engineering,
OCLC Number: 1014233661
Notes: Title from PDF title page (viewed Dec. 7, 2017).
Thesis supervisor: Mo El-Hawary.
Description: 1 online resource (xiv, 94 leaves) : illustrations, maps
Responsibility: by Bader M. Alluhaidah.

Abstract:

ABSTRACT: Decaying fossil fuel resources, international relation complexities, and the risks associated with nuclear power have led to an increased demand for alternative energy sources. Renewable energy sources offer adequate solutions to these challenges. Forecasting of solar energy has also increased over the past decade due to its use in photovoltaic (PV) system design, load balance in hybrid systems, and projected potential future PV system feasibility. Artificial neural networks (ANN) have been used successfully for solar energy forecasting. In this work, several meteorological variables from Saudi Arabia as a case study will be used to determine the most effective variables on Global Solar Radiation (GSR) prediction. Those variables will be used as inputs for a proposed GSR prediction model. This model will be applicable in different locations and conditions. This model has a simple structure and offers better results in terms of error between actual and predicted solar radiation values.

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