Open AccessAccurate investment evaluation model of power grid based on Improved Fuzzy Neural Inference
Author(s) -
Kunpeng Liu,
Lina Gong,
Nuo Tian,
Bo Liu,
Huan Liu
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/827/1/012023
Subject(s) - adaptive neuro fuzzy inference system , computer science , fuzzy logic , data mining , grid , membership function , investment (military) , power (physics) , artificial neural network , table (database) , inference , function (biology) , index (typography) , mathematical optimization , reliability engineering , fuzzy set , operations research , artificial intelligence , engineering , mathematics , fuzzy control system , physics , geometry , quantum mechanics , evolutionary biology , politics , biology , political science , world wide web , law
In the accurate investment evaluation of power grid, the existing model does not consider the uncertainty of the actual evaluation object, and the evaluation subject mainly relies on subjective judgment, resulting in low information utilization rate. Therefore, the accurate investment evaluation model of power grid is designed based on Improved Fuzzy Neural Inference. The evaluation index system is established from three aspects of power supply capacity, power supply quality and power grid benefit of power grid investment, so as to achieve the purpose of giving consideration to both economic and social benefits. Based on the Improved Fuzzy Neural Inference, the membership function algorithm is designed, and the parameters are adjusted to obtain the best fuzzy inference results. According to the membership function and index improvement value, the standardized data table is established. The indicators of the same level are quantified, and the weight attributes are solved to complete the construction of accurate investment evaluation model of power grid. The experimental results show that the design model has certain advantages in the utilization rate of investment information, which is 10.59%, 15.40% and 9.95% higher than the existing models. It proves that the accurate investment evaluation model of power grid constructed in this paper is closer to the actual situation and has good application effect.