Research And Implement On Short-term Load Forecasting Technology Of Electric Power System

Short-term load forecasting of electric power system is not only the basis for the distribution centers to plan power generation and the power plants to quote price, but also an important part of energy management system. Furthermore, short-term load forecasting has all-important effect on power system, such as operation, control and plan. The prediction accuracy has direct influence on economy benefits of the grid and power plants.Electric power is affected by many factors, such as current load status, weather status, festival-holiday, important economy and politics event, and etc. For improving the prediction accuracy of short-term load forecasting, many forecast models applied to power system short-term load forecasting and their characteristics are analyzed, a load forecasting strategy based on RBF neural network and fuzzy logic is proposed considering the integrated effect of many factors. The load forecasting program based on the proposed forecasting strategy is carried out by combined Visual C++ and MATLAB. The results show better prediction accuracy than traditional models.Additionally, both VC++ and MATLAB are adopted in this paper. using the man-machine conversion interface of Visual C++, complicated computation is done at the background with MATLAB tool. so both good man-machine conversation interface and higher exploitation efficiency of software are acquired.