Application method for artificial intelligence in seasonal load prediction

A technology of load forecasting and artificial intelligence, applied in forecasting, data processing applications, neural learning methods, etc., can solve problems such as inaccurate meteorological load calculations

Inactive Publication Date: 2020-04-10
HOHAI UNIV
View PDF0 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide an application method of artificial intelligence in seasonal load forecasting, to solve the inaccurate defect of continuous high temperature in the calculation of meteorological load caused in the prior art

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
  • Application method for artificial intelligence in seasonal load prediction
  • Application method for artificial intelligence in seasonal load prediction
  • Application method for artificial intelligence in seasonal load prediction

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0080] In order to make the technical means, creative features, goals and effects achieved by the present invention easy to understand, the present invention will be further described below in conjunction with specific embodiments.

[0081] Such as Figure 1 to Figure 6 Shown, a kind of application method of artificial intelligence in seasonal load forecasting, described method comprises the following steps:

[0082] Take the acquired meteorological load data as sample data;

[0083] Perform cluster analysis on sample data;

[0084] Input the data obtained from the cluster analysis into the pre-built temperature correction model;

[0085] Input the results of the temperature correction model into the pre-built load forecasting model to get the forecasting results.

[0086] In this embodiment, the method for obtaining meteorological load data includes the following steps:

[0087] Obtain the original load record and extract the original load sequence;

[0088] The trend lo...

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 an application method for artificial intelligence in seasonal load prediction. The application method comprises the steps of: acquiring meteorological load data based on a method class of eliminating trend loads, acquiring a linear trend load function through least square fitting, and subtracting the trend loads from an original load sequence to serve as meteorological loaddata; then performing clustering analysis on the sample data by applying an FCM clustering algorithm so as to obtain three different types of training samples in total; then establishing a temperature correction model, wherein temperature correction needs to be conducted on the highest temperature of the high-temperature weather in consideration of the air temperature accumulation effect of the high-temperature weather in summer; and establishing a PSO-ELM load prediction model, wherein a particle swarm optimization algorithm (PSO) is combined with an extreme learning machine (ELM) to effectively improve the load prediction precision. Thus, an important practical application value is achieved.

Description

technical field [0001] The present invention relates to the technical field of meteorological load forecasting, in particular to an application method of artificial intelligence in seasonal load forecasting, Background technique: [0002] A large number of studies have shown that the relationship between temperature, humidity, wind and other meteorological factors and short-term load changes of electricity is particularly important. The most critical meteorological factor is temperature change, followed by humidity, wind and other factors. With the development and improvement of my country's social and economic level, more and more refrigeration and heating equipment have entered the lives of residents. The increase in the proportion of these temperature-sensitive loads in the total power load will have a significant impact on the power system. Summer cooling loads, The heating load in winter will be directly affected by meteorological factors. For example, rainfall will dire...

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): G06Q10/04G06Q50/06G06N3/08
CPCG06N3/08G06Q10/04G06Q50/06
Inventor 王冰张秋桥王敏张鹏
Owner HOHAI UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap