Grid Computing for Power and Automation Systems Implementations

Abstract

Power load forecasting is the problem which is solved in this paper under MATLAB environment by constructing a neural network for the power load to find simulated solution with the minimum error square. MATLAB code has been programmed for approximating power load data by using the radial based function (RBF) neural network with Gaussian basis function (GBF's). A developed algorithm to achieve load forecasting application with faster techniques is the aim for this paper. The algorithm is used to enable MATLAB power application to be implemented by multi machines in the grid system. Dividing power job into multi tasks job and then to distribute these tasks to the available idle grid contributor(s) to achieve that application within much less time, cheaper cost and with high accuracy and quality. Grid computing, the new computational distributing technology has been used to enhance the performance of power applications to get benefits of idle grid contributor(s) by sharing computational power resources.