Accurate investment evaluation model of power grid based on Improved Fuzzy Neural Inference

Abstract

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.