Research On Photovoltaic Enterprise Value Evaluation

In recent years,the global attention towards the development and utilization of renewable energy has generated new growth opportunities for the photovoltaic(PV)industry.The PV industry is gradually transitioning from being driven by policies and subsidies to being driven by market forces and technological advancements.Compared to traditional manufacturing sectors,PV enterprises possess distinct characteristics such as reliance on policy subsidies,cyclical development,and rapid technological innovation.The dynamic market environment of the PV industry,which is constantly exposed to technological,scale,and product transformations,necessitates corresponding changes in the business models of PV enterprises.Consequently,utilizing traditional evaluation methods to assess the value of PV enterprises may fail to comprehensively capture these changes,leading to inherent limitations.Therefore,when conducting value assessments of PV enterprises,it is imperative to fully consider the unique characteristics of both the PV industry and PV enterprises themselves,and to select appropriate evaluation methods,in order to ensure a rigorous assessment process and achieve more accurate evaluation results.This paper has explored the assessment methods and practical applications of PV enterprises and selected Longi Green Energy as a case for study.First,on the basis of summarizing related literatures at home and abroad,it is found that domestic theoretical and practical studies on the value of PV enterprises started late,and those on the application of BP neural network models to the value assessment of PV enterprises are scarce.The BP neural network model has the advantage of not requiring too much consideration of historical data and being able to handle nonlinear and complex relationships.In this paper,the model has been introduced into the assessment practice of PV enterprises.Combining the characteristics of PV enterprises and the PV industry,based on the value chain theory,the selection of the value drivers of PV enterprises was studied.After completing the main analysis,a value assessment framework was constructed and an index system for assessing the value of PV enterprises was built.After that,representative companies in the PV industry among the 79 companies in the Wind PV Concept Index were incorporated into the model for training by introducing a BP neural network model.Market objective data are analyzed to address subjective issues in selecting comparable indicators and the referenceability of comparable companies in the market approach Finally,a case study was carried out based on the above assessment model,and a BP neural network model was applied to measure the value of the selected enterprise.In addition,a comparative analysis with the evaluation results obtained from the market approach was conducted to further verify the applicability and effectiveness of the BP neural network model.This paper verifies the necessity of the growth,governance and innovation capabilities of PV enterprises for PV enterprise value assessment through model simulation,and demonstrates that the exploration of the application of BP neural networks in the PV industry can help to make more accurate estimates of the value of companies,improve the accuracy and credibility of the assessment results,and provide a more reliable basis for investors and financial institutions to make decisions.