Image phase shift invariance based multi-transform-fusion method for cloud motion displacement calculation using sky images

Zhen, Zhao; Xuan, Zhiming; Wang, Fei; Sun, Rongfu; Duić, Neven; Jin, Tao

doi:10.1016/j.enconman.2019.111853

Cited by 50 publications

(5 citation statements)

References 49 publications

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“…(1) Taking the interval of scanning points t as the variable Taking the average of the 27 sets of calculation results corresponding to a certain t value, and obtaining the relationship between t and n: t and n have a positive correlation, that is, the smaller t is, the smaller n is, but t < 0.05 m will significantly increase the time consumed for scanning and deformation calculation, reducing the applicability of the model, as shown in Figure 6a. (2) Contents of tests When conducting the slope deformation measurement, the parameters that affect the accuracy mainly include the interval of scanning points t, radius of the sphere used for normal estimation R, side length of the prism d, and height of the prism H [51,52]. The commonly used values of these parameters are shown in Table 3.…”

Section: Discussionmentioning

confidence: 99%

“…The displacement of the magnet was controlled by a Vernier caliper, as shown in Figure 5. (2) Contents of tests When conducting the slope deformation measurement, the parameters that affect the accuracy mainly include the interval of scanning points t, radius of the sphere used for normal estimation R, side length of the prism d, and height of the prism H [51,52]. The commonly used values of these parameters are shown in Table 3.…”

Section: Model Testsmentioning

confidence: 99%

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Application of the Terrestrial Laser Scanning in Slope Deformation Monitoring: Taking a Highway Slope as an Example

Yin

Li²,

et al. 2020

Applied Sciences

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Slope deformation monitoring is the prerequisite for disaster risk assessment and engineering control. Terrestrial laser scanning (TLS) is highly applicable to this field. Coarse registration method of point cloud based on scale-invariant feature transform (SIFT) feature points and fine registration method based on the k-dimensional tree (K-D tree) improved iterative closest point (ICP) algorithm were proposed. The results show that they were superior to other algorithms (such as speeded-up robust features (SURF) feature points, Harris feature points, and Levenberg-Marquardt (LM) improved ICP algorithm) when taking the Stanford Bunny as an example, and had high applicability in coarse and fine registration. In order to integrate the advantages of point measurement and surface measurement, an improved point cloud comparison method was proposed and the optimal model parameters were determined through model tests. A case study was conducted on the left side of the K146 + 150 point at S236 Boshan section, Shandong Province, and research results show that from 14 August 2018 and 9 November 2019, the overall deformation of the slope was small with a maximum value of 0.183 m, and the slope will continue to maintain a stable state without special inducing factors such as earthquake, heavy rainfall and artificial excavation.

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Section: Discussionmentioning

confidence: 99%

Section: Model Testsmentioning

confidence: 99%

Application of the Terrestrial Laser Scanning in Slope Deformation Monitoring: Taking a Highway Slope as an Example

Yin

Li²,

et al. 2020

Applied Sciences

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“…Solar irradiance has a direct impact on DPV output [34]. For the ultra‐short‐term scales, the moving cloud is the main factor that affects the amount of irradiance reaching the ground surface, thus having a significant influence on power volatility [35]. The fixed adjacency between DPVs in a graph structure is obviously unreasonable. The spatiotemporal correlation is asymmetric.…”

Section: Spatiotemporal Correlation Analysismentioning

confidence: 99%

Dynamic directed graph convolution network based ultra‐short‐term forecasting method of distributed photovoltaic power to enhance the resilience and flexibility of distribution network

Wang,

Fu,

Zhang

et al. 2023

IET Generation Trans & Dist

Self Cite

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Accurately forecasting regional distributed photovoltaic (DPV) power is crucial in mitigating the negative impact of high DPV penetration on the reliability and resilience of the distribution network. However, most of the current photovoltaic power forecasting methods suffer from two key problems: (1) ignoring the asymmetric influence relationship among DPV sites; (2) lack of consideration of dynamic spatiotemporal correlation among DPV sites. As a result, these methods are unable to fully adapt to the characteristics of DPV, making it challenging to directly apply the existing forecasting methods to improve the accuracy of DPV power forecasting. To conquer this limitation, a dynamic directed Graph Convolution Neural Network (DDGCN) is applied to regional DPV ultra‐short‐term power forecasting. Unlike the conventional Graph Convolution Neural Network (GCN) based forecasting methods, the proposed method improves GCN to process the directed graph. On this basis, to capture the dynamic and directed adjacency relationship among graph nodes, a temporal attention mechanism is proposed and combined with the directed GCN model. In this way, the dynamic and asymmetric/directed relationships among DPV sites can be taken into account. It is worth noting that the DPVs’ adjacency relationship can be constructed without any prior knowledge by end‐to‐end model training. The simulation experiment proves that the prediction accuracy can be further improved by taking into account the dynamic directed relationship among the sites via a real DPV power dataset.

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“…However, due to the chaotic nature of the weather system, the production of photovoltaic energy is highly random, volatile and intermittent, which may lead to grid power and voltage imbalances, and also greatly increase the difficulty of large-scale photovoltaic energy applications [6] [7]. In order to improve the power system's ability to consume photovoltaic energy, many solutions have been proposed, including energy storage optimization [8], demand response strategy [9] [10], power flow optimization [11], stand-alone microgrid [12], and PV power forecasting [13]. Considering economy and feasibility comprehensively, photovoltaic power generation forecast is one of the most promising solutions to the impact of large-scale photovoltaic energy application on the grid [14] [15].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid K-Means-GRA-SVR Model Based on Feature Selection for Day-Ahead Prediction of Photovoltaic Power Generation

Lin¹,

Li²

2021

JCC

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Image phase shift invariance based multi-transform-fusion method for cloud motion displacement calculation using sky images

Cited by 50 publications

References 49 publications

Application of the Terrestrial Laser Scanning in Slope Deformation Monitoring: Taking a Highway Slope as an Example

Application of the Terrestrial Laser Scanning in Slope Deformation Monitoring: Taking a Highway Slope as an Example

Dynamic directed graph convolution network based ultra‐short‐term forecasting method of distributed photovoltaic power to enhance the resilience and flexibility of distribution network

A Hybrid K-Means-GRA-SVR Model Based on Feature Selection for Day-Ahead Prediction of Photovoltaic Power Generation

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