The Research And Realization Of Electric Load Forecast Method

The short-term load forecasting (STLF) of electric power system is one of the important routines for power dispatch and utility departments. It is widely used in the dispatching and operation planning of power systems, and the accuracy of the load forecasting is helpful to the security, economy of power systems and quality of the power supply. With the establishment and development of the power market, STLF has been one of the important symbols for the power system modern managementThis paper comes from my director's project—"Intelligent Electric Power Monitor System". It mainly concerns the change of various kinds of data parameters of the power line. According these parameters, the system can find variational law and forecast the future electricity consumption. With this system, it can insure security of electrical system, steady operation, reasonable distribution of energy sources, so as to save energy sources.The primary problem of the load forecasting is to select one of the forecasting techniques, namely, how to build a forecasting model which is applicable to the studied district. The study of STLF has been deepened and various methods have been put forward, along with the development of the technology. The accuracy has been improved continuously, because of the advanced method from the classical Statistic analysis methods to the modern intelligent methods. However, due to the complexity of the forecasting problem, they have some shortages inevitably.Gray forecasting method is a kind of basal method of load forecasting which needs few load data, not considering distributing rule and transformation trend, convenient operation, high precision of short-term forecasting, easily testing. The task adopts it as the method of load forecasting. The article briefly introduces several load forecasting methods such as artificial neural network, henceforward emphases the gray system theory and gray forecasting model GM(1,1), narrating how to establish forecasting model by making use of gray forecasting method. At last the article shows the forecasting data by table and draws the corresponding curve so as to compare the real data and forecasting data.