Open AccessInvestment demand forecasting model of power grid based on different development paths
Author(s) -
Yi He,
Qin Jiang,
Shujuan Chen,
Yingjie Li,
Xing Zhang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1550/5/052003
Subject(s) - investment (military) , grid , development (topology) , computer science , dimension (graph theory) , path (computing) , matching (statistics) , econometrics , power (physics) , operations research , environmental economics , economics , geography , mathematics , statistics , mathematical analysis , physics , geodesy , quantum mechanics , politics , political science , pure mathematics , law , programming language
Under different economic and social conditions, the development of power grid faces different development foundations, development speeds, development forms, and development goals. The development path and development pattern of the power grid have many commonalities in a certain space-time range, and there are also some natural differences. In terms of analysis of the development path of power grids and the study of the quantification system, which considering the spatial and temporal differences, the research on the spatial-temporal matching of the development path characteristics of the provincial, city, county (district) level units and the prediction of the development pattern is insufficient, and it is impossible to predict the provincial, city, county (district) level. According to the development path characteristics and development forms of provincial, city, and county-level units, this paper establishes a quantitative relationship between grid investment and differential indicators of grid development paths. Based on the analysis of Hunan Province, Changsha, and Changsha County, it is proved that the power grid investment demand prediction models based on different development paths can solve the personalized demand for investment due to differences in power grid development paths, improves the analysis dimension and the level of refinement of investment demand forecasting, and it also provides a reference for forming a scientific investment decision system.