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Recently security issues like investment and financing in China's power industry have become increasingly prominent, bringing serious challenges to the financial security of the domestic power industry. Thus, it deserves to develop financial safety evaluation towards the Chinese power industry and is of practical significance. In this paper, the GA (genetic algorithm) is used to optimize the connection weights and thresholds of the traditional BPNN (back propagation neural network) so the new model of BPNN based on GA is established, hereinafter referred to as GA-BPNN (back propagation neural network based on genetic algorithm). Then, an empirical example of the electric power industry in China during the period 2003–2010 was selected to verify the proposed algorithm. By comparison with three other algorithms, the results indicate the model can be applied to evaluate the financial security of China's power industry effectively. Then index values of the financial security of China's power industry in 2011 were obtained according to the tested prediction model and the comprehensive safety scores and grades are calculated by the weighted algorithm. Finally, we analyzed the reasons and throw out suggestions based on the results. The work of this paper will provide a reference for the financial security evaluation of the energy industry in the future. [ABSTRACT FROM AUTHOR]

This paper attempts to explore the evolvement rule of energy-saving and emission-reduction with changing economic growth rate based on a novel energy-saving and emission-reduction system with multi-variable constraints. Energy structure adjustment, economic growth rate, new energy technology progress and other constrained variables are introduced into the novel system with the aid of nonlinear dynamics. The dynamic behavior of the new system is discussed by means of nonlinear dynamics theory. The quantitative coefficients of the actual system are identified based on the genetic algorithm-back propagation neural network. A scenario study of the actual system is carried out, and some valuable conclusions are presented. Energy structure adjustment is driven more by economic considerations according to China’s national conditions. Energy structure adjustment could perfectly control energy intensity, without obvious potential negative impact on economic growth. New energy technology progress has a better effect on controlling energy intensity, and the more mature the technology is, the better the effect is. Energy intensity is sensitive to economic growth rate. There is an optimal interval of economic growth rate, and energy intensity could be effectively controlled by adjusting corresponding constraint condition in this interval. [ABSTRACT FROM AUTHOR]

The coordinated control systems (CCS) in ultra-supercritical thermal power unit, like many other industrial systems, is a complex multivariable system with severe nonlinearity, strong multivariable coupling and uncertainties. In order to meet the requirements of operational stability, economy. etc in ultra-supercritical unit, it is necessary to establish its accurate mathematical model and further design the advanced controller. Against this background, a new fuzzy neural network modeling method is proposed in this paper. First of all, the incremental model is considered separately to improve the rationality of the local linear model structure. Then, the parameters in antecedent part is initialized by a kernel k -means++ algorithm, in which Xie-Beni index is used to optimize the number of fuzzy rules. Finally, supervised adaptive gradient descent algorithm and artificial immune particle swarm optimization algorithm work in stages to complete the training of the consequent part parameters. The proposed modeling method in this paper is applied to a 1000 MW unit in China and shows satisfactory accuracy. In the established model, the MSE of power output, main steam pressure and separator outlet steam temperature are 0.0099, 1.21E-4, 0.0023, respectively. Both numerical and graphical simulation results confirm the effectiveness of the presented fuzzy neural network in modeling. [ABSTRACT FROM AUTHOR]

Abstract: The three-dimensional energy resources demand-supply system is established for two regions of China, in which determination of unknown parameters is significant for the real energy resources demand-supply system. In this paper, based on Shanghai’s energy statistic data (1999–2005), the parameters of the energy resources demand-supply system can be identified by using the neural network method, and an energy resources demand-supply system reflecting the real energy resources demand-supply relationship in Shanghai is obtained. The dynamical behavior of this system is also analyzed. Theoretical analysis and numerical simulation indicate that the obtained energy resources demand-supply system has the capacity of self-regulation and demonstrates a steady state. Finally, to ensure the steady development of the energy resources demand-supply system, corresponding measures are proposed. [Copyright &y& Elsevier]

Abstract: In this paper, an artificial neural network (ANN) model is developed for estimating monthly mean daily global solar radiation of 8 typical cities in China. The feed-forward back-propagation algorithm is applied in this analysis. The results of the ANN model and other empirical regression models have been compared with measured data on the basis of mean percentage error (MPE), mean bias error (MBE) and root mean square error (RMSE). It is found that the solar radiation estimations by ANN are in good agreement with the measured values and are superior to those of other available empirical models. In addition, ANN model is tested to predict the same components for Kashi, Geermu, Shenyang, Chengdu and Zhengzhou stations over the same period. Data for Kashi, Geermu, Shenyang, Chengdu and Zhengzhou are not used in the training of the networks. Results obtained indicate that the ANN model can successfully be used for the estimation of monthly mean daily global solar radiation for Kashi, Geermu, Shenyang, Chengdu and Zhengzhou. These results testify the generalization capability of the ANN model and its ability to produce accurate estimates in China. [Copyright &y& Elsevier]