Short-term power consumption prediction method based on I-LSTM

A forecasting method and power consumption technology, which is applied in forecasting, neural learning methods, data processing applications, etc., can solve the problems of LSTM model gradient disappearance, can not better capture the ultra-long-term trend of power time series, and improve the accuracy of forecasting The effect of high degree of accuracy, good adaptability, and good overall prediction accuracy

Active Publication Date: 2021-04-09
CHONGQING UNIV OF POSTS & TELECOMM
View PDF20 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the ultra-long memory period makes the LSTM model have the problem of gradient disappearance, so it cannot better capture the ultra-long-term trend of the power time series

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
  • Short-term power consumption prediction method based on I-LSTM
  • Short-term power consumption prediction method based on I-LSTM
  • Short-term power consumption prediction method based on I-LSTM

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0029] Embodiments of the present invention are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation modes, and various modifications or changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the diagrams provided in the following embodiments are only schematically illustrating the basic concept of the present invention, and the following embodiments and the features in the embodiments can be combined with each other in the case of no conflict.

[0030] see Figure 1 to Figure 4 , figure 1 It is a flow chart of a preferred short-term power consumption forecasting method based on I-LSTM in the present...

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 relates to a short-term power consumption prediction method based on I-LSTM, and belongs to the field of power system prediction. The method comprises the following steps: S1, collecting historical data of a power system, and processing incomplete data and abnormal values; S2, dividing the data into a training set, a verification set and a test set according to time; S3, constructing an (Improved Long Short Term Memory) I-LSTM network model, and inputting the training set into the I-LSTM network model for training; S4, setting a network loss function, an optimization algorithm, a learning rate and a batchsize of the I-LSTM network model; and S5, predicting the test set, and obtaining a prediction result of the test set according to a model obtained according to the accurate change condition of the verification set. According to the method, the key information in the user historical data sequence and the characteristic relationship between the user power consumption data can be better mined, and the user power consumption prediction precision and stability are effectively improved.

Description

technical field [0001] The invention belongs to the field of power system prediction, and relates to a short-term power consumption prediction method based on I-LSTM. Background technique [0002] With the improvement of industry and people's living standards, higher requirements are put forward for the supply quantity and supply efficiency of electric energy. Smart grid has been popularized in people's life. Therefore, it is necessary to provide a short-term forecasting method of power system with high accuracy. [0003] With the continuous development of deep learning technology, the power grid field has gradually turned its attention to deep learning. The commonly used method based on deep learning is neural network (Long Short-Term Memory, LSTM). LSTM introduces components with memory function, which can better capture the timing rules before and after data, so it is widely used in timing prediction. However, the ultra-long memory period makes the LSTM model have the ...

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): G06Q10/04G06N3/04G06N3/08G06Q50/06
CPCG06Q10/04G06N3/08G06Q50/06G06N3/045
Inventor 余翔王潇潇庞育才段思睿
Owner CHONGQING UNIV OF POSTS & TELECOMM
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