Small hydropower station group generating capacity prediction method based on big data driving

A technology driven by big data and small hydropower groups, applied in data processing applications, neural learning methods, biological neural network models, etc., can solve the problem that the power generation of small hydropower groups is greatly affected by meteorological factors, so as to increase independence and reduce training Data, the effect of reducing redundancy

Inactive Publication Date: 2020-03-24
GUANGXI POWER GRID CORP +1
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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem in the prior art that the power generation of small hydropower groups is greatly affected by meteorological factors and has great randomness, and proposes a method for predicting the power generation of small hydropower groups based on big data.

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  • Small hydropower station group generating capacity prediction method based on big data driving
  • Small hydropower station group generating capacity prediction method based on big data driving
  • Small hydropower station group generating capacity prediction method based on big data driving

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[0042] The technical solution of the present invention will be described in further non-limiting detail below in conjunction with the embodiments and accompanying drawings.

[0043] The flowchart of the prediction method of the present invention is as figure 1 As shown, the forecasting method of small hydropower group power generation based on big data includes the following steps:

[0044] S1. Divide the small hydropower into multiple small hydropower groups according to the region they belong to, obtain the historical power generation of each small hydropower, and add the hourly historical power generation of the small hydropower in each region to obtain the i-th hour of the small hydropower group in the area Total power generation E Si (i=0,1,2,...23), i is the number of hours; the area is divided into provinces, prefectures, and counties (districts) according to the administrative level, which is convenient for hierarchical scheduling and hierarchical management;

[0045...

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Abstract

The invention discloses a small hydropower station group generating capacity prediction method based on big data driving, and the method comprises the steps: dividing small hydropower station groups according to regions for the current situation that the small hydropower station groups are wide in point range and are managed disorderly; obtaining historical meteorological data and generating capacity data of each region, preprocessing bad data by adopting a data mining technology, and then performing normalization processing; and training the historical meteorological data and the power generation data by using two layers of long-term and short-term memory neural networks and one layer of full-connection neural network, and performing optimization calculation by taking the minimum root-mean-square error of the power generation true value of the test data and the power generation predicted value of the training model as an optimization target to finally obtain the power generation prediction model of the small hydropower station group. According to the method, the generating capacity of the small hydropower station group can be accurately predicted, comprehensive and ordered management of the small hydropower station is realized, and safe and stable operation of a power grid is ensured.

Description

technical field [0001] The invention belongs to the technical field of prediction of hydropower generation, in particular to a method for predicting the generation of small hydropower groups driven by big data. Background technique [0002] In my country, small hydropower refers to hydropower stations with an installed capacity of no more than 25MW. As an internationally recognized green renewable energy, the installed capacity of small hydropower has expanded rapidly in recent years. However, my country's small hydropower stations have problems such as small installed capacity, multiple locations, difficult maintenance, and decentralized management. Compared with the scientific dispatch and standardized management of large hydropower stations, small hydropower stations are basically still in a state of disordered management. Disorderly management leads to the emergence of isolated islands of information among dispatching agencies at all levels, which makes small hydropower...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06N3/04G06N3/08G06Q50/06
CPCG06Q10/06375G06Q50/06G06N3/08G06N3/044G06N3/045Y04S10/50
Inventor 黄馗吴剑锋陈晓兵韦化张乐祝云张弛吕中梁黄国辉江雄烽练椿杰覃圣超
Owner GUANGXI POWER GRID CORP
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