Considering The Weather Conditions, Power System Short-term Load Forecasting

The level of load forecasting is one of the measures of modernization of power system management. So load forecasting, especially accurate short-term load forecasting is of great importance to power system. There are many factors that affect system load, such as history data of load, many non-load factors in which weather factor is the most important.The outlier identification is divided into two sequential parts: the robust day-load-curves cluster and the bad curve pattern classification. By analyzing the effects of Kohonen network clustering and BP network classification, the dissertation designs an outlier identification model comprising these two kinds of neural network and implements the tasks of bad data identifications and adjustments.In order to forecast normal day load, this thesis advances a integrated model consisting of time series model, fuzzy model and linear regression model through studying the relations between load and weather; In order to forecast the load of such day which temperature change sharply, this thesis uses the 3r model to adjust the former two models and the forecasting results validate the effectivity of the adjustment; In order to forecast festal day load, this thesis uses the neighboring weekends load data while considering temperature factor and uses gray model to forecast the load of the day after the feast.A single model can only make use of limited information and the information that the models used can't be the completely same, so a EW integrated forecasting model is proposed in the end of the thesis to improve the accurateness. All modeling and forecasting are based on the data of Zhejiang province load and the program is applied in the power dispatch center of Zhejiang province and it's performance is satisfactory.