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A bus short-term daily load forecasting method and device combining clustering and deep learning algorithms

A deep learning, bus load technology, applied in forecasting, machine learning, computing and other directions, can solve problems such as large volume and large number of buses, and achieve the effect of accurate prediction results, high prediction accuracy and improved accuracy

Active Publication Date: 2022-03-04
STATE GRID SHANDONG ELECTRIC POWER +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Aiming at the deficiencies in the existing technology and solving the problem of large number of buses and wide coverage, one or more embodiments of the present disclosure provide a method and device for short-term daily load forecasting of buses combined with clustering and deep learning algorithms , effectively improve the accuracy of busbar short-term daily load forecasting

Method used

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  • A bus short-term daily load forecasting method and device combining clustering and deep learning algorithms
  • A bus short-term daily load forecasting method and device combining clustering and deep learning algorithms
  • A bus short-term daily load forecasting method and device combining clustering and deep learning algorithms

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Embodiment 1

[0054]According to an aspect of one or more embodiments of the present disclosure, a bus short-term daily load forecasting method combining clustering and deep learning algorithms is provided.

[0055] Such as figure 1 As shown, a bus short-term daily load forecasting method combining clustering and deep learning algorithms, the method includes:

[0056] Step S1: Analyze the load characteristics of the grid bus, and determine the short-term bus load forecasting influencing factors;

[0057] Step S2: Extract the characteristics of the influencing factors, perform data standardization processing, and establish a load database;

[0058] Step S3: using a big data clustering algorithm to aggregate the busbars with similar characteristics together to complete the pattern classification and determine the K value;

[0059] Step S4: According to the K patterns in step S3, establish a corresponding prediction model through deep learning long-short-term memory network;

[0060] Step S...

Embodiment 2

[0093] According to an aspect of one or more embodiments of the present disclosure, there is provided a computer-readable storage medium.

[0094] A computer-readable storage medium, in which a plurality of instructions are stored, and the instructions are suitable for being loaded and executed by a processor of a terminal device to execute the bus short-term daily load forecasting method combining clustering and deep learning algorithms.

Embodiment 3

[0096] According to an aspect of one or more embodiments of the present disclosure, a terminal device is provided.

[0097] A terminal device, which includes a processor and a computer-readable storage medium, the processor is used to implement instructions; the computer-readable storage medium is used to store multiple instructions, and the instructions are suitable for being loaded by the processor and executing the described one A Bus Short-term Daily Load Forecasting Method Combining Clustering and Deep Learning Algorithms.

[0098] These computer-executable instructions, when executed in a device, cause the device to perform the methods or processes described in accordance with various embodiments in the present disclosure.

[0099] Among other things, a computer program product may include a computer-readable storage medium having computer-readable program instructions for carrying out various aspects of the present disclosure thereon. A computer readable storage medium...

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Abstract

The present disclosure discloses a method and device for short-term daily load forecasting of a bus that combines clustering and deep learning algorithms. The method includes: receiving grid bus data, analyzing the load characteristics of the grid bus, and determining the short-term bus load forecasting influence factor; extracting the influence factor characteristics, data standardization processing, and establishment of a load database; clustering algorithms are used to aggregate buses with similar characteristics to determine the K value; the prediction models corresponding to K modes are established through deep learning long-term and short-term memory networks; the Momentum algorithm is used to optimize the prediction model , complete bus load forecasting.

Description

technical field [0001] The disclosure belongs to the technical field of load forecasting of power grid dispatching departments, and relates to a method and device for short-term daily load forecasting of busbars combined with clustering and deep learning algorithms. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] The accuracy of bus load prediction results will significantly affect safety checks and day-ahead planning. In order to ensure the safe, stable and economical operation of the power system and avoid unnecessary energy waste, it is necessary to grasp the changing laws and development trends of various loads. The bus load prediction results can provide hypothetical power flow data for the power grid, which is the basis for safety and stability analysis, reactive power optimization, dynamic state estimation, local control of power p...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q50/06G06K9/62G06N20/00
CPCG06Q10/04G06Q50/06G06N20/00G06F18/23G06F18/214
Inventor 焦敏李康刘恒杰亓晓燕胡昌伦孟凡敏刘啸宇王涛许晓敏王文君陈霖陈泽伟陈爱友梁龙飞秦子健丁吉峰张方芬李新蕾
Owner STATE GRID SHANDONG ELECTRIC POWER