Sustainable Investment Forecasting of Power Grids Based on theDeep Restricted Boltzmann Machine Optimized by the Lion Algorithm

This paper proposes a new power grid investment prediction model based on the deep restricted Boltzmann machine(DRBM)optimized by the Lion algorithm(LA).Firstly,two factors including transmission and distribution price reform(TDPR)and 5G station construction were comprehensively incorporated into the consideration of influencing factors,and the fuzzy threshold method was used to screen out critical influencing factors.Then,the LA was used to optimize the parameters of the DRBM model to improve the model’s prediction accuracy,and the model was trained with the selected influencing factors and investment.Finally,the LA-DRBM model was used to predict the investment of a power grid enterprise,and the final prediction result was obtained by modifying the initial result with the modifying factors.The LA-DRBMmodel compensates for the deficiency of the singlemodel,and greatly improves the investment prediction accuracy of the power grid.In this study,a power grid enterprise was taken as an example to carry out an empirical analysis to prove the validity of the model,and a comparison with the RBM,support vector machine(SVM),back propagation neural network(BPNN),and regression model was conducted to verify the superiority of the model.The conclusion indicates that the proposed model has a strong generalization ability and good robustness,is able to abstract the combination of low-level features into high-level features,and can improve the efficiency of the model’s calculations for investment prediction of power grid enterprises.

Multi-Criteria Decision Making Based on Correlation Coefficient of Triangular Intuitionistic Fuzzy Numbers

We study a multi-criteria fuzzy decision-making method based on weighted triangular intuitionistic fuzzy number correlation coefficients. Under the scenario that criteria weights for alternatives are completely unknown, triangular intuitionistic fuzzy method can not only supplement the insufficiency of the method based on the distance but also endow more information to the estimation and reduce the loss of evaluation information.Among the triangular numbers, two boundary numbers are the maximum and minimum values of the interval respectively, and the medium number is the most possible value under subjective estimation. Using this method,we propose a new way to obtain the criteria weights with more information quantity. By ranking the relative closeness of the weighted correlation coefficients between each alternative, and the critical and ideal alternatives,we show the method to figure out the most suitable alternative based on the expected criteria. An illustrative example is also taken into account to prove the effectiveness of the model.

EFFICIENCY DECOMPOSITION WITH SHARED INPUTS AND OUTPUTS IN TWO-STAGE DEA

数据包封分析(DEA ) 是为与多重输入和产量测量决策单位(DMU ) 的相对效率的一个有效非参量的方法。处于许多真实状况， DMU 的内部结构是二阶段的网络进程，分享的输入在两个阶段生产的阶段和普通产量使用了。例如，医院有二阶段的网络结构。舞台 1 消费象产生象医药记录，洗衣店和家务那样的产量的信息技术系统，植物，设备和主管人员那样的资源。舞台 2 消费舞台 1 使用的资源(命名分享的输入) 和舞台 1 产生的产量(命名中间的措施) 的一样的集合提供耐心的服务。而且，一些例如，输出耐心的满足度，被二个单个阶段一起产生(命名分享的产量) 。因为一些分享的输入和产量努力被分开并且分配到各单个的舞台，它需要为处于如此的问题评估二阶段的网络进程的性能开发二阶段的 DEA 方法。这份报纸扩大集中的模型测量二阶段的过程的 DEA 效率与非， splittable 分享了输入和产量。一条加权的添加剂途径被用来联合二个单个阶段。而且，添加剂效率分解模型被开发同时评估最大并且为单个阶段的最小的可完成的效率。最后，中国建设的分支在安徽省存的 17 城市的一个例子被采用说明建议途径。