Impact of cost-benefit analysis on financial benefit evaluation of investment projects under back propagation neural network

被引:18
|
作者
Jin, Xin [1 ]
Liu, Qian [2 ]
Long, Huizhen [3 ,4 ]
机构
[1] Guilin Tourism Univ, Sch Business, Guangxi Tourism Res Inst, Guilin 541006, Peoples R China
[2] Hohai Univ, Business Sch, Changzhou 213022, Peoples R China
[3] Hong Kong Shue Yan Univ, Dept Econ & Finance, Hong Kong, Peoples R China
[4] Columbia Univ, Sch Arts & Sci, New York, NY 10027 USA
关键词
Power transmission and transformation projects; Financial benefits evaluation; BPNN; Break-even balance; Sensitivity; PARTICLE SWARM OPTIMIZATION; GRID INVESTMENT; FUZZY-LOGIC; POLICY; MODEL; TRANSMISSION; EFFICIENCY; SUBSIDY; DESIGN;
D O I
10.1016/j.cam.2020.113172
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
In order to realize the financial benefit evaluation of the investment project, the investment project of power transmission and transformation of a power grid enterprise in Sichuan province is taken as an example. First, the overall cost of the investment project is analyzed and introduced, and the sales revenue, running costs, taxes and surcharges in the four years from 2016 to 2019 are calculated, and the financial benefit evaluation index and calculation method are determined. Then, the Fuzzy optimal back propagation neural network (BPNN) is constructed based on BPNN and fuzzy optimization strategy for cost-benefit analysis. Then an improved Gravitational search algorithm (IGSA) algorithm based on GSA algorithm is proposed applied to the optimization of fuzzy optimal BPNN. Finally, the influence of some uncertainty factors on the financial benefit of investment project is measured through break-even analysis and sensitivity analysis. The results show that the operation index of the substation is stable and the electricity sold shows an increasing trend after its operation. The financial situation of the investment project is optimistic with high economic benefits. The internal rate of return of the capital can reach 17.13%, and the payback period of the investment is less than 7 years. The sensitivity of the internal rate of return of project investment from high to low is unit price of electricity sold, electricity sold and operation cost. The output value of the samples trained by the fuzzy optimal BPNN is close to the expected value, with a small error of about 0.3% at most, and the training speed is fast and the training effect is good. The actual output result of the financial evaluation index is basically consistent with the expected output result of the expert survey, with a small error. Moreover, the application of the model to other similar investment projects still has good financial benefit evaluation results and good generalization ability. In conclusion, BPNN can be used to calculate the cost and benefit and predict the financial benefit of investment projects. According to the forecast results, a feasible investment scheme is proposed, which can be used for reference in the financial benefit evaluation of similar investment projects. (C) 2020 Elsevier B.V. All rights reserved.
引用
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页数:15
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