Semantic Segmentation Algorithm-Based Calculation of Cloud Shadow Trajectory and Cloud Speed

Cloud covering is an important factor affecting solar radiation and causes fluctuations in solar energy production. Therefore, real-time recognition and the prediction of cloud covering and the adjustment of the angle of photovoltaic panels to improve power generation are important research areas in...

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Vydané v:Energies (Basel) Ročník 15; číslo 23; s. 8925
Hlavní autori: Wang, Shitao, Sun, Mingjian, Shen, Yi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.12.2022
Predmet:
Algorithms
Artificial intelligence
cloud moving prediction
cloud shadow
cloud speed
Clouds
Deep learning
Digital cameras
Environmental aspects
Machine learning
Methods
Neural networks
Photovoltaic power generation
Propagation
Radiation
semantic segmentation algorithm
Semantics
Software
Solar energy
China
ISSN:1996-1073, 1996-1073
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Shrnutí:Cloud covering is an important factor affecting solar radiation and causes fluctuations in solar energy production. Therefore, real-time recognition and the prediction of cloud covering and the adjustment of the angle of photovoltaic panels to improve power generation are important research areas in the field of photovoltaic power generation. In this study, several methods, namely, the principle of depth camera measurement distance, semantic segmentation algorithm, and long- and short-term memory (LSTM) network were combined for cloud observation. The semantic segmentation algorithm was applied to identify and extract the cloud contour lines, determine the feature points, and calculate the cloud heights and geographic locations of the cloud shadows. The LSTM algorithm was used to predict the trajectory and speed of the cloud movement, achieve accurate and real-time detection, and track the clouds and the sun. Based on the results of these methods, the shadow area of the cloud on the ground was calculated. The recursive neural LSTM network was also used to predict the track and moving speed of the clouds according to the cloud centroid data of the cloud images at different times. The findings of this study can provide insights to establish a low-cost intelligent monitoring predicting system for cloud covering and power generation.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1996-1073
1996-1073
DOI:10.3390/en15238925