Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Model-data hybrid driven bilateral electricity market electricity price prediction method

A power market, hybrid drive technology, applied in market forecasting, market data collection, data processing applications, etc., can solve the problems of short contract duration, low renewal rate, difficult time series, etc., to achieve stable model parameters and solve problems. The effect of fast speed and high prediction accuracy

Pending Publication Date: 2022-04-19
DALIAN UNIV OF TECH
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In addition, bilateral contracts rely on the contracting relationship between the buyer and the seller. Due to the homogeneity of electricity commodities, this relationship is usually difficult to maintain. Market players often change contracting partners in the next delivery period, and the contracting relationship shows a strong randomness. sex and chance
This results in a short duration of buyer-seller contracts, a low renewal rate, and it is difficult to form a long time series
The above-mentioned characteristics of the two-sided market make it difficult to apply the traditional electricity price forecasting method, and the direct analysis of the price itself also loses a stable carrier. Therefore, it is necessary to find other indirect carriers and a relatively stable expression relationship between the indirect carrier and the electricity price.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Model-data hybrid driven bilateral electricity market electricity price prediction method
  • Model-data hybrid driven bilateral electricity market electricity price prediction method
  • Model-data hybrid driven bilateral electricity market electricity price prediction method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0066] The present invention will be further described below in conjunction with the accompanying drawings and examples of implementation.

[0067] The flow process of the inventive method is as figure 1 As shown, the principle of extracting the kernel network is as follows figure 2 As shown, the transaction price range between the purchaser and the seller is as follows image 3 shown. The algorithm is written in python language (version 3.7).

[0068] This implementation case takes the contract data set of the monthly bilateral market in Yunnan Province in my country from 2016 to 2018 as an example to verify the method proposed in the present invention. Yunnan Province is the first batch of pilot provinces in my country's electric power market reform, and it is also one of the provinces with the highest marketization rate in my country. In 2020, Yunnan's electricity market-based electricity accounted for nearly 60% of the total electricity consumption of the society, and...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a model-data hybrid driven bilateral electricity market electricity price prediction method. The method specifically comprises the following steps: firstly, providing a bilateral market data set sampling method based on nuclear network sampling; then establishing a data regression model by using an optimization method by means of a basic idea of data driving, and reversely solving value estimation of a single market subject through mutual constraint among multiple market subjects; the price of the negotiation contract which does not occur is predicted by using the determined value estimation parameter; and finally, screening different prediction models generated according to different data sets to obtain a final prediction model. According to the invention, the price of the contract which is not reached in the electricity market can be predicted, and reference can be provided for operation supervision of the market and making of own power generation and power utilization plans by a market subject.

Description

technical field [0001] The invention belongs to the field of electricity price forecasting in the electricity market, and in particular relates to electricity price prediction in a bilateral electricity market contract under the condition of insufficient market data types and data volumes, specifically a model-data hybrid-driven electricity price prediction method in a bilateral electricity market. [0002] technical background [0003] The electricity market is one of the important means to achieve the goal of "carbon neutrality and carbon peak". Since the "new electricity reform" in 2015, China has continued to promote the reform of the power market. In 2020, the direct transaction power of the national power market will reach 2.476 billion kWh, accounting for 32.9% of the total power consumption of the whole society. The power market has gradually become an electric energy supply- Main approaches and technical solutions to be matched. Bilateral trading is one of the most ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/02G06Q50/06G06K9/62
CPCG06Q30/0201G06Q30/0206G06Q50/06G06F18/214
Inventor 程春田李亚鹏韩旭王祥祯于申申建建
Owner DALIAN UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com