Abstract
Under the new situation, with the continuous development of my country's economy and the implementation of power system reforms, higher development requirements have been put forward for the distribution network investment plan. Through the scientific and reasonable calculation of the investment scale of the distribution network, optimizing the investment scale of the distribution network and rationally arranging the investment planning of the distribution network project have become one of the key concerns of the current power grid enterprises. This paper uses fishbone diagram theory to analyze the factors that affect the investment scale of the distribution network, and selects the key factor indicators to construct a distribution network investment trend prediction model based on support vector machines. By selecting a certain region's distribution network investment for empirical forecasting analysis, and comparing with the planned investment of the distribution network in the region, the validity of the model is verified.
Highlights
As a basic power facility, the distribution network plays an extremely important role in the development of the economy and the improvement of people's living standards
Strengthening the investment forecast management of the distribution network is of vital importance to improving the operating efficiency of power grid enterprises
Literature [2] based on a comprehensive analysis of the current status of distribution network management, combined with historical data, obtained some problems in the distribution network investment planning of county-level power supply enterprises, and used the gray prediction method and the multiple linear regression prediction model to carry out Load forecasting avoids the shortcomings of a single forecasting method, and achieves the purpose of reducing forecasting risks and increasing forecasting accuracy
Summary
As a basic power facility, the distribution network plays an extremely important role in the development of the economy and the improvement of people's living standards. Literature [2] based on a comprehensive analysis of the current status of distribution network management, combined with historical data, obtained some problems in the distribution network investment planning of county-level power supply enterprises, and used the gray prediction method and the multiple linear regression prediction model to carry out Load forecasting avoids the shortcomings of a single forecasting method, and achieves the purpose of reducing forecasting risks and increasing forecasting accuracy. This paper first applies the fishbone diagram theory to systematically analyze the main factor indicators that affect the distribution network investment; combines the relevant expert opinions and the measurability of the factor indicator data to select the key factor indicators to construct a support vector machine-based distribution network investment forecast Model; the relevant data of a certain area distribution network is selected as a sample to verify the validity and feasibility of the model
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