Short-term Load Forecasting Study Based On Neural Network

Short-term load forecasting of electric power system is a very important job for the distribution centers, it has important effect on power system, such as operation, control and plan. The prediction accuracy has direct influence on economy benefits of the gird and power plants.Purpose of the load forecasting, developments and trends of the research were reviewed at first briefly; then constituents,classification and load periodic change regulations of load were detailedly introduced, every kind of factor of load of impact was analyzed. Since the traditional BP algorithm has some unavoidable disadvantages, such as slow training speed and possibility of local minimizing the optimized function, an optimized L-M algorithm, which can accelerate the training of neural network and improve the stability of the convergence, should be applied to create short-term load forecasting models. And the network was trained with historical electric power load data, resulted successful short-term electric power load forecast.The simulation showed that the forecasting results were the comparatively scientific forecasting methods with high precision. This method has been used in dispatch load forecasting.