Power load forecasting method, system, and storage medium based on deep learning

A deep learning and power load technology, applied in the field of electricity consumption, can solve the problems of improving prediction accuracy, unusable time series methods, and the need to improve the accuracy of machine learning methods, so as to achieve high prediction accuracy and improve prediction accuracy.

Active Publication Date: 2021-06-01
HEFEI UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

There are some defects in the above traditional forecasting methods. For example, traditional time series methods cannot use other relevant data other than power load to improve forecasting accuracy. The accuracy of traditional time series methods and machine learning methods needs to be improved.

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  • Power load forecasting method, system, and storage medium based on deep learning
  • Power load forecasting method, system, and storage medium based on deep learning
  • Power load forecasting method, system, and storage medium based on deep learning

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Embodiment Construction

[0027] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0028] In the first aspect, the embodiment of the present invention provides a power load forecasting method based on deep learning, such as figure 1 As shown, the method includes:

[0029] S101. Collect the user's power load data, meteorological data, and air quality data within a preset historical time period, and divide the collected data into...

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Abstract

The invention provides a deep learning-based power load forecasting method, system, and storage medium, and relates to the technical field of power consumption. The method includes: S101. Collecting power load data, meteorological data, and air quality data of users within a preset historical time period, and dividing the collected data into training sets and test sets; S102. Deep learning model; S103. Input the test set into the deep learning model for power load forecasting to obtain the power load forecast data of the user in the third time interval. The present invention uses deep learning to predict power load, and not only considers power load data but also meteorological data and air quality data during the deep learning process, which can improve the accuracy of power load forecasting.

Description

technical field [0001] The present invention relates to the technical field of electricity consumption, and in particular to a deep learning-based power load forecasting method and system, and a storage medium. Background technique [0002] With the continuous development of the power system, the power system is becoming more and more important to the economic development of the society. With the continuous advancement of power grid technology and the increasing demand for electricity in the economy and society, electric energy services have now covered many fields. In this context, the normal operation of the power grid system is very important, and power demand forecasting is of great significance to the operation of the power grid system. The complexity and variability of the power grid system determine the need for strong self-adaption and comparative high accuracy. [0003] At present, there are two main methods of power system load forecasting: one is to predict the ...

Claims

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Application Information

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Patent Type & AuthorityPatents(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N20/00
CPCG06Q10/04G06Q50/06G06N20/00
Inventor周开乐郭志峰杨善林李兰兰陆信辉
OwnerHEFEI UNIV OF TECH