AC-DC power distribution network load prediction method based on ensemble learning

A technology of load forecasting and integrated learning, applied in the direction of AC network circuits, electrical components, circuit devices, etc., can solve the problems of reducing forecasting accuracy, increasing load forecasting generalization error, overfitting, etc., to reduce forecasting errors, The effect of reducing generalization error and improving accuracy

Active Publication Date: 2020-01-17
NANJING UNIV OF SCI & TECH
View PDF1 Cites 11 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

A single model is prone to overfitting, increasing the generalization error of load forecasting, thereby reducing the forecasting accuracy, and a single machine learning model is sensitive to load anomalies, and is more prone to overfitting for load forecasting of AC and DC distribution networks Phenomenon

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
  • AC-DC power distribution network load prediction method based on ensemble learning
  • AC-DC power distribution network load prediction method based on ensemble learning
  • AC-DC power distribution network load prediction method based on ensemble learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0059] In order to verify the effectiveness of the scheme of the present invention, the historical load data of an AC and DC power distribution system in Jiangsu Province from May 2018 to December 2018 were selected to conduct the following simulation experiments.

[0060] 1) Perform data preprocessing on the original load data

[0061] The data sampling interval is 15 minutes, the missing value data is interpolated, and finally the maximum and minimum normalization processing is performed. After data cleaning, about 15,000 training sample data sets are obtained, and the length of the sliding time window is set to 8, that is, the load data of 8 data points is used to predict the load size at the next moment. Part of the training data is shown in Table 1.

[0062] Table 1 Part of the training data

[0063]

[0064] 2) Establish an integrated learning model based on gradient boosting algorithm

[0065] In the ensemble learning model of the gradient boosting algorithm, the ...

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 AC / DC power distribution network load prediction method based on ensemble learning, and the method comprises the steps of carrying out the load data filling and normalization on the original load data, sampling a load sample input vector and a sample label through a sliding time window, and constructing a training data set; establishing a gradient lifting model, settingthe number of weak learners, and establishing a plurality of shallow neural networks to fit the negative gradient of the gradient lifting algorithm to obtain a combined prediction model; and selectinga load vector before a to-be-predicted time point as an input vector by using the sliding time window, and determining a load prediction value in combination with an ensemble learning model. According to the invention, the load prediction is carried out by fusing the strong learners of multiple models, so that the precision of load prediction is improved.

Description

technical field [0001] The invention relates to the field of load forecasting of an AC-DC distribution network, in particular to an integrated learning-based load forecasting method for an AC-DC distribution network. Background technique [0002] With the grid integration of new energy in the distribution network and the rapid development of power electronic equipment, the energy distribution in the power grid has undergone great changes. Since the access of different types of loads in the AC-DC distribution network will lead to sharp changes in load power, accurate load forecasting is required to ensure the optimal dispatch of the AC-DC hybrid distribution network, which is important for the safe and stable operation of the distribution network. significance. Traditional load forecasting methods mainly revolve around a single machine learning model, such as support vector regression, neural network and other models. A single model is prone to overfitting, increasing the g...

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): H02J5/00H02J3/00
CPCH02J5/00H02J3/00Y04S10/50
Inventor 柳伟杨镇宁朱肖镕李娜阮思洁徐洲张俊芳
Owner NANJING UNIV OF SCI & TECH
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