Short Term Load Forecasting a Case Study of Kota City

Short-term load forecasting is an important component in the power system load forecast, it is very important to unit optimum combination, economic scheduling, optimum current of dispatching department. Classical load forecasting methods include time sequence, regression method, and so on, but many of them have defects, for example, numerical value is instability and the factor which influences load can't be considered. Artificial intelligence method is main method now; neural network BP algorithm is representative among of them. When using neural network to predict electric power load, front neural network can predict with more precision fitting high linking and non-linear relation of shining upon between inputting and outputting from complicated sample data through studying. However some new problems have appeared while predicting electric power load using this method, it can't distinguish the impact on load data of the influence factor clearly, network structure can't be optimized and fixed automatically and need to confirm network structure artificially, the result is easy to fall into local optimum. So General Regression Neural Network- GRNN is proposed in this paper, it achieves global optimizing and can sample or calculate the data obtained to revise the network directly under the same structure, it need not calculate the parameter again, but only need a simple smooth parameter, it needn't carry on the training course of circulation.