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

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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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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