Application Of Data Mining In The Load-Forecast System

This thesis introduces the relevant knowledge of Load-Forecast at first, then recommends the existed Load-Forecast models in detail, and analyzes the pluses and minuses of the various models. Under the circumstances that the developed of the Power-Industry, we have accumulated lots of diversified relevant data of load. We put forward a load-forecast model that based on Data-Mining. And then we introduce the effect of the Data-Mining used in the model, we choose the ANN as the main technology. In order to improve the BP Arithmetic's study rate, we do some improvement, and get more accurate results. Then we introduce the improved GA which used to improve the BP Arithmetic. Another DM technology--Fuzzy theory which will be used in the Pre-prepare of the load and relative data. Utilize the advantage of BP Arithmetic and GA, we establish a load-forecast model based on improved ANN and GA. We set 96-point to make the load of a day. Based the model we develop a software whit friendly UI. The average forecast result is above 99. 5%.The system has already been put in practice in Beijing, Heilongjiang and HeBei power office successfully.