The net load demand of the distribution network is large,the environmental sensitivity is high,and the reliability of short-term load forecasting is poor.Therefore,the user side short-term load time series forecasting model based on the im-proved SLIQ algorithm is studied.The SLIQ algorithm is adopted to classify and process the massive load data including cli-mate change,analyze the uncertainty of demand response on the user side,input the classified data and user demand response rate into the combined forecasting model based on time series,and preliminarily forecast the short-term load results on the user side.The neural network model of S-type function is used to improve the solution process of SLIQ algorithm.The gradient search learning method is used to correct the calculation error of S-type excitation function,adjust the uncertainty of demand re-sponse on the user side,and output the short-term load forecasting results on the user side.The test results show that the model is feasible to complete the classification of historical load data after combining meteorological conditions.The accuracy and fitting coefficient of daily load forecasting are above 0.95 and 0.94,respectively,which can reliably complete the load fore-casting on the user side.
关键词
改进SLIQ算法/用户侧/短期负荷/时间序列/预测模型/预测结果修正
Key words
improved SLIQ algorithm/user side/short-term load/time series/forecasting model/revision of forecasting result
维普
万方数据