Research On Prediction And Application Of Power Grid Enterprises Investment Capability Under The Environment Of New Electricity Reform

The development of electric power is an important cornerstone of economic development.The development of social economy and the improvement of people’s living standards all put forward high requirements for electric power.The whole society’s demand for electric power is increasing,and the consumption of electric power is growing.As an infrastructure industry,the power industry is related to the overall social development and people’s livelihood.As the connection platform between power plants and users,it is very important to strengthen the infrastructure construction of power grid.With the development of new electricity reform,the electricity price mechanism and the profit model of power grid enterprises have changed significantly in the power market,and the operation of power grid enterprises will be subject to strict cost investment supervision.At the same time,the complex environmental changes put forward higher requirements for power grid investment management,which makes it urgent to improve the scientificity and lean of investment and asset management,so as to adapt to the changes of regulatory requirements as soon as possible.Therefore,the research on prediction and application of power grid enterprises investment capability under the environment of new electricity reform can enhance the theoretical basis of the investment plan of power grid,so as to guide the power grid enterprises to accurately grasp the investment law of power grid projects and maximize the benefits of power grid enterprises,which is of great significance to the sustainable development of power grid enterprises.Firstly,this paper summarizes the research status of domestic and foreign scholars in terms of power grid investment capacity and intelligent prediction technology,analyzes the relevant theories of power grid enterprise investment capacity,and combs the power grid enterprise transmission and distribution price verification methods.Then,the paper sums up the influencing factors of the investment capacity of power grid enterprises from six aspects:financial situation,development level of the company,operating efficiency,market environment,transmission and distribution price reform,potential influencing factors,and selects ten key influencing factors by using the fuzzy threshold method.At the same time,the research on the investment capacity analysis model of power grid enterprises based on transmission and distribution price is carried out.The short-term investment capacity prediction model of power grid enterprises based on system dynamics and the medium or long-term investment capacity prediction model of power grid enterprises based on FA-DRBM are constructed.In the short-term investment capacity prediction model of power grid enterprises based on system dynamics,the investment capacity is divided into profit subsystem and depreciation subsystem,financing subsystem and current assets subsystem,and the system dynamics is used to build the model.The empirical analysis and sensitivity analysis are carried out.In the medium or long-term investment capacity prediction model of power grid enterprises based on FA-DRBM,the firefly algorithm is used to optimize the Deep Restricted Boltzmann Algorithm,and the optimized model is used to carry out the investment capacity.The research on the investment capability of power grid enterprises in this paper is an important part of the investment management of power grid enterprises and an important step to promote the investment lean of power grid enterprises.Based on the new electricity reform,this paper makes a deep research on the dynamic relationship among power grid investment capacity,investment and transmission and distribution price,and proposes the prediction model of power grid enterprises’ investment capacity from different perspectives,which provides a new method and reference for the prediction and analysis of the investment capacity of power grid enterprises.