Citation: Wang, S.; Sun, M.; Shen, Y.
Semantic Segmentation
Algorithm-Based Calculation of
Cloud Shadow Trajectory and Cloud
Speed. Energies 2022, 15, 8925.
https://doi.org/10.3390/en15238925
Academic Editors: Luis
Hernández-Callejo,
Sergio Nesmachnow and
Sara Gallardo Saavedra
Received: 24 October 2022
Accepted: 23 November 2022
Published: 25 November 2022
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Article
Semantic Segmentation Algorithm-Based Calculation of Cloud
Shadow Trajectory and Cloud Speed
Shitao Wang, Mingjian Sun and Yi Shen *
Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
* Correspondence: shen@hit.edu.cn; Tel.: +86-451-86413411-8602; Fax: +86-451-86418378
Abstract:
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.
Keywords:
solar energy; semantic segmentation algorithm; cloud moving prediction; cloud shadow;
cloud speed
1. Introduction
Solar energy is a widely distributed and sustainable source of energy worldwide.
Photovoltaic power generation technology can directly convert light energy into electrical
energy through the photovoltaic effect, and it has the advantages of no pollution, safe use,
and convenient maintenance. With continuous technical improvement and cost reduction,
photovoltaic power generation has increased rapidly. In 2005, the global cumulative
installed photovoltaic capacity exceeded 5 GW. According to “Snapshot of Global PV
Markets 2020” [
1
] issued by the International Energy Agency, by the end of 2019, the global
installed capacity exceeded 600 GW, and the average annual growth rate was 41%. In the
past three years (2019–2022), the annual installed capacity has exceeded 100 GW. Figure 1
shows the global installed photovoltaic capacity over the past 10 years (2011–2019).
Large-scale photovoltaic projects require real-time monitoring of power quality and
operating information while maintaining optimal scheduling. Therefore, it is essential to
ensure the accurate forecasting of generation capacity, especially short-term and real-time
forecasting [
2
]. Therefore, dynamically adjusting the solar panel according to weather type,
cloud occlusion, and the radiation angle of sunlight to maximize the power generated
by photovoltaic modules has always been an important research topic in the field of
photovoltaic power generation [
3
–
5
]. Changes in photovoltaic power generation are almost
proportional to the changes in radiation intensity, which are directly affected by cloud
occlusion. Different weather types and cloud cover lead to considerable changes in the
power generated by photovoltaic systems and power grid fluctuations [6–9].
However, in the field of photovoltaic power generation, it has always been challenging
to accurately predict the weather type and cloud movement [10,11].
Energies 2022, 15, 8925. https://doi.org/10.3390/en15238925 https://www.mdpi.com/journal/energies
