Wind energy is a reliable, clean source and has emerged as one of the dependable, and the best performing developing renewable energy around the world. It has insignificant environmental impacts, compared to other energy sources. In Sarawak, Malaysia, wind resource varies depending on the location. An inadequate number of wind stations are the major obstacles that slow down the growing of green energy in the region. Site selection is a crucial issue for potential investors and policy makers. This paper examines the spatial distribution and the amount of potential wind power and energy densities for wind energy production and suitable locations in Sarawak. A geographical Information System (GIS) assisted methodology, which includes wind speed, power and energy densities using the existing wind station and based on the newly developed prediction model called topographical neural network (TNN) were used. Kriging interpolation was employed for a simple interpolation of data between locations. The results show that the northeast, northwest and coastal regions have better prospects of wind energy. The studied GIS methodology can be applied for identification of the most suitable locations for wind energy harvesting. The developed maps can further be used in micro-siting and economic evaluation analysis.
In this paper, the time series, and a parametric feedforward neural network model were designed. A methodology for wind speed prediction in the regions where wind speed is not available by measurement based on the T-FFNN is proposed in this work according to the meteorological, topographical and geographical parameters for long-term prediction. Typical wind speed and direction are respectively predicted by the optimum 9-152-1 T-FFNN. Then the prediction results are analyzed. The results show that the suggested approach is powerful and can be used effectively to predict the wind speed and direction. The observed and modeled data were used in developing the energy map using ArcGIS 9.3 which shows the distribution of wind speed and power density across the studied area.
In this paper, a hybrid of Finite difference-Simpson’s approach was applied to solve linear Volterra integro-differential equations. The method works efficiently great by reducing the problem into a system of linear algebraic equations. The numerical results shows the simplicity and effectiveness of the method, error estimation of the method is provided which shows that the method is of second order convergence.
The approximate solutions for the semibounded Hadamard type hypersingular integrals (HSIs) for smooth density function are investigated. The automatic quadrature schemes (AQSs) are constructed by approximating the density function using the third and fourth kinds of Chebyshev polynomials. Error estimates for the semibounded solutions are obtained in the class of ℎ( ) ∈ , [−1, 1]. Numerical results for the obtained quadrature schemes revealed that the proposed methods are highly accurate when the density function ℎ ( ) is any polynomial or rational functions. The results are in line with the theoretical findings.
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