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...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.展开更多
With the development of artificial intelligence, communication, computer and other related technologies, it becomes feasible to rebuild traditional railway with such advanced technologies in order to establish a new g...With the development of artificial intelligence, communication, computer and other related technologies, it becomes feasible to rebuild traditional railway with such advanced technologies in order to establish a new generation railway transport system. The railway intelligent transportation system is the trend of railway transportation system in China, and it is also the research focus of international railway transport industry. This paper presents the definition, characters, architecture, key technologies and developing pattern of the RITS(railway intelligent transportation system). Then three typical applications are introduced. Finally, the prospect of the RITS is summarized.展开更多
基金Projects(70572090, 70373017) supported by the National Natural Science Foundation of China
文摘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.
基金funded by the National Natural Science Foundation of China ( No. 61074151) Nation Science and Technology Support Program ( No. T1DB300020 and No. T1DB200010)Ministry of Railways Science and Technology Research Program ( No. 2006X023 and No. 2010X008)
文摘With the development of artificial intelligence, communication, computer and other related technologies, it becomes feasible to rebuild traditional railway with such advanced technologies in order to establish a new generation railway transport system. The railway intelligent transportation system is the trend of railway transportation system in China, and it is also the research focus of international railway transport industry. This paper presents the definition, characters, architecture, key technologies and developing pattern of the RITS(railway intelligent transportation system). Then three typical applications are introduced. Finally, the prospect of the RITS is summarized.
