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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| Main Author: | |
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| Format: | Thesis |
| Language: | English English |
| Published: |
1999
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/9458/1/FSAS_1999_16_A.pdf |
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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. |
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