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A method for predicting investment demand of power network

A demand forecasting and power grid technology, applied in forecasting, computing models, biological models, etc., can solve problems such as low search efficiency, easy to fall into local optimum, slow convergence speed, etc., to achieve strong generalization ability and robustness, The effect of a good predictive effect

Pending Publication Date: 2019-01-15
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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Problems solved by technology

The above algorithms are helpful for the optimization of support vector machine parameters, but there are also obvious shortcomings, such as low search efficiency, slow convergence speed, and easy to fall into local optimum

Method used

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  • A method for predicting investment demand of power network
  • A method for predicting investment demand of power network
  • A method for predicting investment demand of power network

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Embodiment Construction

[0023] The embodiments will be described in detail below in conjunction with the accompanying drawings.

[0024] 1. Construction of the influencing factors system for power grid investment forecasting

[0025] China's power grid investment forecast is affected by many factors. In order to realize the accurate forecast of China's power grid investment, the present invention preliminarily selects the relevant influencing factors of power grid investment forecast on the basis of consulting a large number of documents, and through the Delphi method, the industry Several rounds of anonymous consultation and feedback were conducted on the opinions of experts to finally determine the influencing factor system for China's power grid investment forecast. The present invention constructs a power grid investment forecast influencing factor system from four dimensions of economic development, electric power demand, grid scale and grid benefit. Industrial proportion), population, urbaniza...

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Abstract

The invention belongs to the technical field of power network investment demand prediction, in particular to a power network investment demand prediction method, comprising the following steps of: constructing an influence factor system of power network investment prediction through a Delphi method; calculating the grey relational degree of each influence factor and the power network investment, sorting and screening out the main influence factors of the power network investment forecast as the input of the forecast model; the parameters of support vector machine are optimized by differentialevolution improved grey wolf optimization algorithm, and the investment demand of power network is forecasted after the forecasting model is established. The empirical analysis proves that the improved gray-wolf algorithm based on differential evolution has strong generalization ability and robustness to support vector machine optimization model in power network investment forecasting, and can achieve good forecasting effect, which provides a new idea for the research of power network investment forecasting.

Description

technical field [0001] The invention belongs to the technical field of power grid investment demand forecasting, and in particular relates to a power grid investment demand forecasting method. Background technique [0002] At present, the power grid is playing an increasingly important role in social and economic development. It bears the important responsibility of optimizing the allocation of energy resources and promoting social development, and is an important implementer of energy strategies. The development of the economy promotes the increasing demand for power grid investment. Accurate and effective forecasting of power grid investment demand can not only help to coordinate funds and rationally arrange the investment of power grid construction funds, but also reduce capital costs and economic risks, and promote the process of grid investment planning and construction. , play a vital role. In recent years, scholars at home and abroad have used various forecasting mod...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/00G06Q10/04G06Q50/06
CPCG06N3/006G06Q10/04G06Q50/06G06F18/2411
Inventor 牛东晓戴舒羽李偲厉艳
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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