A novel recurrent neural network forecasting model for power intelligence center

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.

Measuring Research on County Agricultural Technological Innovation Ability Index

Taking the mechanism of technological construction guidance theory and mode which consists of "objective-construction-evaluation-construction-objective" as a starting point, on the basis of county agricultural technological innovation ability and its index definition, this paper researches the constructing system of county agricultural technological innovation ability. Firstly, on the basis of defining county agricultural technological innovation ability and the definition of index, according to the principle of purposefulness, scientificity, systematicness, integration of dynamic state and static state, integration of quantitativeness and qualitativeness and so on, we construct the multi-level measuring system of county agricultural technological innovation ability, including 4 first-level indices, namely technological innovation environment, technological innovation basis, technological innovation ability, and technological innovation efficiency; 15 second-level indices, such as technological policy, technological system mechanism, technological institution construction, ability of innovation subject, ability of industrial expansion, scale merit, technological contribution rate. Moreover, this system has 45 third-level indices. Then, by using unascertained mathematics method and AHM method, we establish the multi-level unascertained composite measuring model of county agricultural technological innovation ability index. Finally, by using the survey data of one county in Hebei Province, and the established county agricultural technological innovation ability index model, we get the county agricultural technological innovation ability index of 0.711 by calculation, that is, the innovation ability is at the intermediate level, namely the modern agricultural sub-stage. The empirical research proves the correctness and applicability of this model.