The Evolvement Of Index Influencing By Improving Association And Efciency Assessment

In our country electric power industry is run by state monopoly. As a result, manyelectric power enterprises have poor production efciency and operating performance.In order to improve the benefits of the electric power enterprises, efective performanceevaluation methods are taking into accounting. NMG Power Group Co.,Ltd, a whollystate-owned power grid company in Inner Mongolia autonomous region, is the onlyprovincial State grid corporation alone. Efciency evaluation must be diferent withother power grid companies according to its distinct characteristics.First of all, a new set of efciency evaluation indexes was put forward on theground of the unique positioning of NMG Power Group Co.,Ltd. The indexes systemincluded five input and one output indicators. Based on2011datum of nine groupcompany subordinates, gamma cumulative probability distribution function was usedto standardization. In order to be more scientific, development potential would be paidattention to as the same as the current situation. With history datum of six indicatorsfrom1987to2011, Multiple State Space and Vector ARMA models were fitted andpredicted values of2015were calculated.Full Permutation Polygon Synthesis Index, an intuitive method and its improvedformula were chosen. Indicators of2011and2015were separately applied to orderefciencies of nine subordinates. Rank correlation coefcients and mutual informationcoefcients were evaluated and edge correlations were considered to compute trianglearea. Results showed that the operating efciency of WH power and EEDS’s werebetter than others. The efciencies of the improved method were higher than ones ofthe original method.To improve the accuracy of efciency evaluation, the advanced function estimationmethods in other fields were quoted and samples needed to be established. Each indexwas divided into five levels and30input samples were chosen by Optimal Design. Theoutput efciencies of them were evaluated through Delphi method. Rule Ensemble, Sixalgorithms, Random Forest, Stochastic Gradient Boosting, Support Vector, ArtificialNeural Network, Multivariate Adaptive Spines Regression and Linear Regression, wereapplied to obtain efciencies. The average of six values was used as final ranks in thelight of combination forecast. It was showed that EEDS power and WH power had more efciency than others. These two bureaus should be paid more attention to analyze andpopularize.Based on the analysis of the type and characteristics of risk in electric power mar-ket, Grey System, Multiple Linear Regression, VaR and Markov Chain were employedto measure all kinds of risk. Risk management processes were put forward. The statusof wind power in NMG were analyzed by SWOT and Analytic Hierarchy Process.