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Power load forecasting is the problem which is solved in this paper under M\ATLAB environment by constructing a neural network for the power load to find simulated solution with the minimum error square. NIATLAB 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 M\ATLAB 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.

The need for reliable, powerful, and clean power generation in power systems is becoming more importance. This need requires geographically-distributed power systems to be integrated as a single entity where among the main features of this integration are large data base and computing intensive. Hence the current power systems are not able to handle this huge datasets required for that integration, Grid Computing is a gateway to virtual storage media and processing power. This paper describes the research work on why grid computing is needed for power systems, what are the major challenges and problem to implement grid computing into power systems, and how grid computing can be utilized to fulfil the requirement for efficient power generation. Keyword

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