The depreciation scale of fixed assets is an important parameter that affects the operational profits of power grid enterprises and transmission and distribution pricing. The forecasting of depreciation scale is of immense significance for power grid enterprises to comprehend their profitability and the transmission and distribution pricing trends, in advance. In this paper, we analyze the main factors affecting the depreciation level of fixed assets in provincial power grids, distinguish the structure of existing assets and incremental assets, build a depreciation forecasting model, and further conduct an empirical study to quantitatively analyze the influence of different business strategies on depreciation. The study results can support management and decision-making optimization of power grid assets and provide support for scientific decision-making by government departments.
Energy is an important material basis for guaranteeing and promoting economic growth and social development. In order to predict the future energy demand for regional energy in Liaoning Province, this paper uses the gray forecast model to make reasonable predictions on regional energy in Liaoning. This paper first establishes a gray prediction model, analyzes the applicable environment and conditions of the gray prediction model, and secondly uses the gray prediction model to predict the energy of Liaoning Province from 2010 to 2019, and compares it with the actual data and elastic coefficient method. It shows that the prediction accuracy based on the gray prediction model is significantly higher than that of the traditional elasticity coefficient prediction method, which proves the scientificity and effectiveness of the model for regional energy demand prediction. Finally, on this basis, this paper uses the grey prediction model to analyze the energy of Liaoning Province from 2020 to 2029. The method and data can provide reliable data basis for the planning of Liaoning power grid and the evolution of the future power grid.
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