System model for predicting electricity price based on support vector regression algorithm

A technology of support vector regression and electricity price, which is applied in market forecasting, calculation, instrumentation, etc.

Pending Publication Date: 2020-11-20
上海积成能源科技有限公司
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  • System model for predicting electricity price based on support vector regression algorithm
  • System model for predicting electricity price based on support vector regression algorithm
  • System model for predicting electricity price based on support vector regression algorithm

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[0007] In order to make the content, purpose, features and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the implementation of the following descriptions Examples are only some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the protection specification of the present invention, and the specific steps for the operation of the entire system are as follows.

[0008] Step 1. Obtain the hourly historical temperature, working days, holidays, hourly parameters, monthly parameters, annual parameters, and historical electricity load in the area by measuring or obtaining historical data, including ...

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Abstract

Along with continuous promotion of a new round of power market reform, the electricity price serves as an important index reflecting the market operation condition, the electricity price is accuratelypredicted, the power market game party can be helped to conduct risk prevention, and economic benefit maximization is achieved. A gradient boosting decision tree is a model which is prevalent in machine learning, the main idea of the gradient boosting decision tree is that a weak classifier (decision tree) is used for iterative training to obtain an optimal model, and the model has the advantagesof being good in training effect, not prone to over-fitting and the like. The method is widely applied in the industry, supports high-efficiency parallel training, and has the advantages of higher training speed, lower memory consumption, higher accuracy, support of distribution, capability of quickly processing mass data and the like. According to the method, the node electricity price is predicted based on the support vector regression algorithm, and therefore the prediction accuracy can be greatly improved.

Description

technical field [0001] The invention relates to the field of electricity price prediction, in particular to a system model for predicting electricity price based on a support vector regression algorithm. Background technique [0002] With the official start of the electricity market reform, the new electricity reform has opened up the electricity sales side of the electricity market, making my country's electricity market gradually market-oriented. As a special product in the electricity market, electricity prices reflect the operation of the electricity market, An important indicator for evaluating the competition efficiency of the electricity market is the basis for scientific decision-making by all parties in the electricity market. Accurate forecasting of electricity price data can help electricity sales companies determine the day-ahead quotation in the market, avoid risks to the greatest extent, and improve returns. It provides price signals for the power generation capa...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/02G06Q50/06
CPCG06Q30/0201G06Q30/0202G06Q30/0206G06Q50/06
Inventor 胡炳谦顾一峰周浩韩俊
Owner 上海积成能源科技有限公司
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