T.O. Halawani

Estimation of global solar radiation using artificial neural networks

This paper introduces a neural network technique for the estimation of global solar radiation. There are 41 radiation data collection stations spread all over the kingdom of Saudi Arabia where the radiation data and sunshine duration information are being collected since 1971. The available data from 31 locations is used for training the neural networks and the data from the other 10 locations is used for testing. The testing data was not used in the modeling to give an indication of the performance of the system in unknown locations. Results indicate the viability of this approach for spatial modeling of solar radiation.

A neural networks approach for wind speed prediction

This paper introduces neural networks technique for wind speed prediction and compares its performance with an autoregressive model. First, we studied the statistical characteristics of mean monthly and daily wind speed in Jeddah, Saudi Arabia. The autocorrelation coefficients are computed and the correlogram is found compatible with the real diurnal variation of mean wind speed. The stochastic time series analysis is found to be suitable for the description of autoregressive model that involves a time lag of one month for the mean monthly prediction and one day for the mean daily wind speed prediction. The results on a testing data indicate that the neural network approach outperforms the AR model as indicated by the prediction graph and by the root mean square errors.

USE OF RADIAL BASIS FUNCTIONS FOR ESTIMATING MONTHLY MEAN DAILY SOLAR RADIATION

The present study utilizes the radial basis functions technique for the estimation of monthly mean daily values of solar radiation falling on horizontal surfaces and compares its performance with that of the multilayer perceptrons network and a classical regression model. In this work, we use solar radiation data from 41 stations that are spread over the Kingdom of Saudi Arabia. The solar radiation data from 31 locations are used for training the neural networks and the data from the remaining 10 locations are used for testing the estimated values. However, the testing data were not used in the modeling or training of the networks to give an indication of the performance of the system at unknown locations. Results indicate the viability of the radial basis for this kind of problem.

Weibull parameters for wind speed distribution in Saudi Arabia

The shape and scale parameters of a Weibull density distribution function are calculated for 10 locations in Saudi Arabia. The daily mean wind speed data from 1970 to mid-1990 are used for this purpose. It is found that the numerical values of the shape parameter vary between 1.7 and 2.7, whereas the value of the scale parameter is found to vary between 3 and 6. It is also concluded from this study that wind data are very well represented by the Weibull distribution function.

Wind power cost assessment at twenty locations in the kingdom of Saudi Arabia

The Kingdom of Saudi Arabia has vast open land and hence has great potential of harnessing solar and wind energy sources for domestic and industrial use. This study proposes to assess wind power cost per kWh of electricity produced using three types of wind electric conversion systems at 20 locations within the Kingdom. These sites cover the eastern, central, and western regions. Hourly values of wind speed recorded for periods of 5.5–13 years (between 1970–1982, in most cases) were used for all 20 locations. Wind duration curves were developed and utilized to calculate the cost per kWh of electricity generated from three chosen wind-machines.

Statistical characteristics of wind in Saudi Arabia

The statistical characteristics of wind at 10 locations in the Kingdom of Saudi Arabia are studied. The autocorrelation coefficients are computed and correlograms are found compatible with the real diurnal variation of mean wind speed for almost all the locations. The stochastic time series analysis is found to be suitable for the description of autoregressive models involving time lags of 1 and 24 h. The forecast wind values obtained from these autoregressive models are compared with the observed wind data for almost all the locations and are found to be in very good agreement.

Global solar radiation estimation

This paper presents the comparison between the observed and estimated values of global solar radiation on horizontal surfaces obtained from the linear Angstrom type of correlation of Rehman and Halawani [1] for 52 cities spread in 11 countries; viz. India, Egypt, Sri Lanka, Spain, Zimbabwe, Yemen, Sudan, Italy, Zambia, Hong Kong, and Malaysia. The comparisons are also made with the local linear models wherever available. The estimated values are compared with the measured values in terms of percent error, mean bias error (MBE), mean absolute bias error (MABE), root mean square error (RMSE), and mean percent error (MPE). This study finds that the model of Rehman and Halawani [1] is capable of giving estimates within an acceptable mean percent error of 5% and less for 33% of cities and between 5–10% for 50% of cities.

Automation of the Arabic sign language recognition

This paper introduces a system to recognize the Arabic sign language using an instrumented glove and a machine learning method. Interfaces in sign language systems can be categorized as direct-device or vision-based. The direct-device approach uses measurement devices that are in direct contact with the hand such as instrumented gloves, flexion sensors, styli and position-tracking devices. On the other hand, the vision-based approach captures the movement of the singers hand using a camera that is sometimes aided by making the signer wear a glove that has painted areas indicating the positions of the fingers or knuckles. The proposed system basically consists of a PowerGlove that is connected through the serial port to a workstation running the support vector machine algorithm. Obtained results are promising even though a simple and cheap glove with limited sensors was utilized.

Smart card for smart campus: KFUPM case study

Smart card is the latest addition in the world of information technology. The vision of the smart card program is to provide access to services that is secure, fast, friendly, easy to use, flexible, personal, and is accessible by the users from any place at any time. A smart card is of the size of a conventional credit card with an embedded computer chip that stores and transacts data between users and devices. This data is associated with either value or information or both and is stored and processed within the chip of the card. The card data is transacted via a card reader attached to a computing system as a peripheral device. Smart cards are extensively used through several key applications like education, healthcare, banking, entertainment, and transportation.

Radiative fluxes in the troposphere from upper air meteorological data in Saudi Arabia

Abstract An attempt is made to use the empirical type of parameterization scheme for the calculations of solar radiative fluxes and atmospheric heating or cooling rates in the troposphere by using observed upper air meteorological data. This type of parameterization scheme is useful in calculating the upper air heating/cooling rate for use in planetary boundary layer modelling and in the evaluation of the surface heat budget equation. The results presented in this paper are based on the upper air data collected between 1300 to 1500 Local Standard Time (LST) in Dhahran for the year 1989. The diurnal variation of vertical profiles of radiative fluxes and heating/cooling rates is calculated by changing the solar zenith angle. The diurnal variation of annually averaged solar radiation fluxes reaching the ground is compared with the observed values of solar radiative flux at Dhahran. The calculated values are in excellent agreement with the observed values in the morning and evening times. The scheme used in this paper can be used for obtaining solar radiative fluxes and atmospheric heating or cooling rates above the earths surface at places where only upper air meteorological data is available.