Comparison of different UV models for cloud effect study

Wang, Lunche; Gong, Wei; Luo, Ming; Wang, Wenfeng; Hu, Bo; Zhang, Ming

doi:10.1016/j.energy.2014.12.026

Cited by 10 publications

(6 citation statements)

References 57 publications

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“…Reference [18] uses CI to separate forecasting complexity into the prediction of solar geometry and the prediction of cloudiness and aerosol. The quadratic and cubic equations which are based on global solar irradiance data have the highest accuracy in predicting the diffuse fraction as a function of CI [19][20][21].…”

Section: Literature Reviewmentioning

confidence: 99%

Daily Clearness Index Profiles Cluster Analysis for Photovoltaic System

Lai

Jia

McCulloch

et al. 2017

IEEE Trans. Ind. Inf.

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Due to various weather perturbation effects, the stochastic nature of real-life solar irradiance has been a major issue for solar photovoltaic (PV) system planning and performance evaluation. This paper aims to discover clearness index (CI) patterns and to construct centroids for the daily CI profiles. This will be useful in being able to provide a standardized methodology for PV system design and analysis. Four years of solar irradiance data collected from Johannesburg (26.21S, 28.05E), South Africa are used for the case study. The variation in CI could be significant in different seasons. In this paper, cluster analysis with Gaussian Mixture Models, K-Means with Euclidean distance, K-Means with Manhattan distance, Fuzzy C-Means with Euclidean distance and Fuzzy C-Means with Dynamic Time Warping (FCM DTW) are performed for the four seasons. A case study based on sizing a stand-alone solar PV and storage system with anaerobic digestion biogas power plants is used to examine the usefulness of the clustering results. It concludes that FCM DTW and GMM can determine the correct PV farm rated capacity with an acceptable energy storage capacity, with 36 and 46 rather than 1457 solar irradiance profiles respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Daily Clearness Index Profiles Cluster Analysis for Photovoltaic System

Lai

Jia

McCulloch

et al. 2017

IEEE Trans. Ind. Inf.

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“…Under the same atmospheric conditions, a longer transmission path leads to a greater degree of radiation attenuation, which lowers the ground erythemal UV radiation. For example, erythemal UV radiation decreases with an increase in SZA [13]. Clouds and aerosols within the atmosphere attenuate erythemal UV-B radiation reaching the surface because of scattering and absorption [14,15].…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have estimated the erythemal UV-B from remote sensing data. Remote estimation of UV-B has been developed in the past few decades [13], and they can be divided into two categories: radiative transfer (RT) process-based methods and statistical methods.…”

Section: Introductionmentioning

confidence: 99%

“…Conventional statistical methods often focus on a single ground site [29] by establishing the relationship between satellite observations and ground measurements. Wang et al [13] used the UV radiation observation data at Yanting, China, to establish the regression relationship between the hourly observed UV irradiation and relative optical air mass, cloud modification factor, and clearness index; the root mean square error (RMSE) under all-sky conditions was 10.3%. Liu et al [30] analyzed the dependence of UV irradiation on the clearness index and SZA.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of 1-km Resolution All-Sky Instantaneous Erythemal UV-B with MODIS Data Based on a Deep Learning Method

Zhao

2022

Remote Sensing

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Although ultraviolet-B (UV-B) radiation reaching the ground represents a tiny fraction of the total solar radiant energy, it significantly affects human health and global ecosystems. Therefore, erythemal UV-B monitoring has recently attracted significant attention. However, traditional UV-B retrieval methods rely on empirical modeling and handcrafted features, which require expertise and fail to generalize to new environments. Furthermore, most traditional products have low spatial resolution. To address this, we propose a deep learning framework for retrieving all-sky, kilometer-level erythemal UV-B from Moderate Resolution Imaging Spectroradiometer (MODIS) data. We designed a deep neural network with a residual structure to cascade high-level representations from raw MODIS inputs, eliminating handcrafted features. We used an external random forest classifier to perform the final prediction based on refined deep features extracted from the residual network. Compared with basic parameters, extracted deep features more accurately bridge the semantic gap between the raw MODIS inputs, improving retrieval accuracy. We established a dataset from 7 Surface Radiation Budget Network (SURFRAD) stations and 1 from 30 UV-B Monitoring and Research Program (UVMRP) stations with MODIS top-of-atmosphere reflectance, solar and view zenith angle, surface reflectance, altitude, and ozone observations. A partial SURFRAD dataset from 2007–2016 trained the model, achieving an R2 of 0.9887, a mean bias error (MBE) of 0.19 mW/m2, and a root mean square error (RMSE) of 7.42 mW/m2. The model evaluated on 2017 SURFRAD data shows an R2 of 0.9376, an MBE of 1.24 mW/m2, and an RMSE of 17.45 mW/m2, indicating the proposed model accurately generalizes the temporal dimension. We evaluated the model at 30 UVMRP stations with different land cover from those of SURFRAD and found most stations had a relative RMSE of 25% and an MBE within ±5%, demonstrating generalization in the spatial dimension. This study demonstrates the potential of using MODIS data to accurately estimate all-sky erythemal UV-B with the proposed algorithm.

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“…Another approach is to develop empirical models for different wavelengths of radiation using sky conditions (i.e., K T ) determined from the more commonly measured global solar radiation. These include models for ultraviolet radiation [10][11][12][13][14][15], erythemal ultraviolet radiation [16][17][18][19], photosynthetically active radiation (PAR) [20][21][22], and near-infrared radiation [23]. Using these models, the effects of several atmospheric parameters on the transmission of solar radiation have been assessed, including cloud cover [14,16,24,25], the presence of aerosols [9,26,27], and ozone concentration [12,19].…”

Section: Introductionmentioning

confidence: 99%