A novel recurrent neural network forecasting model for power intelligence center  

作　　者：刘吉成 牛东晓 

机构地区：[1]School of Business Administration, North China Electric Power University,Beijing 102206, China

出　　处：《Journal of Central South University of Technology》2008年第5期726-732,共7页中南工业大学学报（英文版）

基　　金：Projects(70572090, 70373017) supported by the National Natural Science Foundation of China

摘　　要：In order to accurately forecast the load of power system and enhance the stability of the power network, a novel unascertained mathematics based recurrent neural network (UMRNN) for power intelligence center (PIC) was created through three steps. First, by combining with the general project uncertain element transmission theory (GPUET), the basic definitions of stochastic, fuzzy, and grey uncertain elements were given based on the principal types of uncertain information. Second, a power dynamic alliance including four sectors: generation sector, transmission sector, distribution sector and customers was established. The key factors were amended according to the four transmission topologies of uncertain elements, thus the new factors entered the power intelligence center as the input elements. Finally, in the intelligence handing background of PIC, by performing uncertain and recursive process to the input values of network, and combining unascertained mathematics, the novel load forecasting model was built. Three different approaches were put forward to forecast an eastern regional power grid load in China. The root mean square error (ERMS) demonstrates that the forecasting accuracy of the proposed model UMRNN is 3% higher than that of BP neural network (BPNN), and 5% higher than that of autoregressive integrated moving average (ARIMA). Besides, an example also shows that the average relative error of the first quarter of 2008 forecasted by UMRNN is only 2.59%, which has high precision.In order to accurately forecast the load of power system and enhance the stability of the power network, a novel unascertained mathematics based recurrent neural network （UMRNN） for power intelligence center （PIC） was created through three steps. First, by combining with the general project uncertain element transmission theory （GPUET）, the basic definitions of stochastic, fuzzy, and grey uncertain elements were given based on the principal types of uncertain information. Second, a power dynamic alliance including four sectors： generation sector, transmission sector, distribution sector and customers was established. The key factors were amended according to the four transmission topologies of uncertain elements, thus the new factors entered the power intelligence center as the input elements. Finally, in the intelligence handing background of PIC, by performing uncertain and recursive process to the input values of network, and combining unascertained mathematics, the novel load forecasting model was built. Three different approaches were put forward to forecast an eastern regional power grid load in China. The root mean square error （ERMS） demonstrates that the forecasting accuracy of the proposed model UMRNN is 3% higher than that of BP neural network （BPNN）, and 5% higher than that of autoregressive integrated moving average （ARIMA）. Besides, an example also shows that the average relative error of the first quarter of 2008 forecasted by UMRNN is only 2.59%, which has high precision.

关 键 词：load forecasting uncertain element power intelligence center unascertained mathematics recurrent neural network 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]