Heliostat artificial intelligence correction method, device, electronic equipment and storage medium

Abstract

The present disclosure relates to a method, device, electronic device and storage medium for artificial intelligence correction of heliostats. The method includes: reading sample data; training an LSTM network according to the sample data to obtain an LSTM network model; The data is input into the LSTM network model, and the correction data of the heliostat is output after being calculated by the LSTM network model; and the daily tracking operation parameters of the heliostat are corrected using the correction data of the heliostat. Heliostat sample data is efficiently learned using methods including long short-term memory artificial intelligence machine learning, allowing heliostats to track with higher accuracy.

Description

technical field

[0001] The present disclosure relates to the technical field of solar energy applications, and more particularly to a method, device, electronic device and storage medium for artificial intelligence correction of heliostats. Background technique

[0002] Solar energy is used more and more in people's lives because of its advantages of large total energy, wide distribution, cleanliness, no pollution and high economy. In the process of utilizing solar energy, whether using solar photovoltaic panels, solar concentrating mirrors or other forms, in order to receive more solar radiation for use, solar tracking technology is increasingly applied. Especially in large-scale solar power plants, solar tracking systems can be used to improve the power generation efficiency of solar power plants to increase production.

[0003] For example, when the solar tracking technology is applied in solar photovoltaic power generation, its control system calculates according to the...

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