Modeling of Meteorological Parameters for Jordan

Jordan is a developing country with limited natural resources. It imports most of its energy for heating, electric power generation, and other uses. The limited energy sources consider renewable energy options such as solar, wind, and hydropower as alternatives. For successful energy research and...

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Bibliographic Details
Main Author: Ahmed Alghoul, Mohammad
Format: Thesis
Language:English
English
Published: 1999
Subjects:
Online Access:http://psasir.upm.edu.my/id/eprint/9458/1/FSAS_1999_16_A.pdf
http://psasir.upm.edu.my/id/eprint/9458/
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Summary:Jordan is a developing country with limited natural resources. It imports most of its energy for heating, electric power generation, and other uses. The limited energy sources consider renewable energy options such as solar, wind, and hydropower as alternatives. For successful energy research and applications, weather parameters (wind speed, sunshine duration, humidity_ temperature, and global solar radiation) should be modeled. For solar energy applications, information on global solar radiation are required for the country over a long period of time. Some sites have no records of solar radiation data. So, developing methods to predict solar radiation from any available weather data are necessary. Models based on Angstrom formula using data such as Sunshine, Temperature and Humidity of four stations are described. Wind is an important energy resource and man has long sought to harness it. The calculation of the output of a wind machine requires knowledge of the distribution of the wind speed. Weibull distribution was applied to fit the probability distribution nature of wind speed. For all locations the wind speed data can be modeled easily by the Weibull distribution. Time series analyses of the weather parameters such as wind speed, relative sunshine duration, relative humidity, range in temperature extrenies, and relative solar radiation all as daily average were carried out. To apply AutoRegressive process, transformation technique (Differencing) was applied to generate stationary time series. Seasonal and non-seasonal AutoRegressive models of order p AR(P) were used to describe the weather parameters data for all stations.