Bias induced by the AOD representation time scale in long-term solar radiation calculations. Part 2: Impact on long-term solar irradiance predictions

Ruiz‐Arias, José A.; Gueymard, Christian A.; Santos-Alamillos, Francisco J.; Quesada-Ruiz, Samuel; Pozo‐Vázquez, D.

doi:10.1016/j.solener.2016.06.017

Cited by 13 publications

(3 citation statements)

References 35 publications

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“…The difference of clearness index attenuation among Tianjin, Xi'an and Zhengzhou was mainly attributed to the higher attenuation coefficient (absolute value of a in Table 5), and the attenuation coefficient was related to the air pollutants and mass concentration of particulate matters in the local air. When the concentration of particulate matters was the same, the smaller the particle size, the more significant the extinction of sunlight [35,36]. In addition, the regional monsoon climate accelerated the migration and accumulation of particulate matters in the air, resulting in the same fluctuations of clearness index in September and October in the four cities.…”

Section: Evaluation Of Monthly Solar Radiation Attenuationmentioning

confidence: 98%

An investigation on the attenuation effect of air pollution on regional solar radiation

et al. 2020

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Due to the continuous increase of environmental pollution in recent years, the high concentration of particulate matters in air has greatly reduced the amount of solar radiation that can reach the earth, and this reduction has a direct effect on the use of solar energy in buildings. To quantify this attenuation effect, historical meteorological data collected from five regions, namely, Beijing, Tianjin, Jinan, Xi'an and Zhengzhou, in China from 2014 to 2016 were used to investigate the correlation between clearness index (reflecting available radiation) and air quality index (reflecting pollution level). The analysis results have revealed that higher air quality index would result in lower clearness index, and the sunny days gave higher decreasing rate than cloudy days. For all five regions, their monthly clearness index attenuation showed higher values in winter than that in summer. The monthly solar radiation attenuation, however, showed an opposite trend, due to higher solar altitude in summer. Additionally, different regions had different annual solar radiation attenuation ratio, with Tianjin giving the highest of 6.56% (651.17MJ/m 2 ), followed by Beijing (3.92%, 410.08MJ/m 2 ), Xi'an (4.94%, 510.42MJ/m 2 ), Zhengzhou (3.99%, 427.64MJ/m 2 ) and Jinan (2.69%, 284.66MJ/m 2 ).

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Section: Evaluation Of Monthly Solar Radiation Attenuationmentioning

confidence: 98%

An investigation on the attenuation effect of air pollution on regional solar radiation

et al. 2020

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“…There is a rich array of literature describing many methods of predicting solar irradiance with artificial neural networks (ANNs) [12][13][14][15][16][17][18][19][20]. Kamadinata et al [12] predicted solar irradiance based on sky image data.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Applicability of Impact Factors to Estimate Solar Irradiance: Comparative Analysis Using Machine Learning Algorithms

et al. 2021

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This study explores investigation of applicability of impact factors to estimate solar irradiance by four machine learning algorithms using climatic elements as comparative analysis: linear regression, support vector machines (SVM), a multi-layer neural network (MLNN), and a long short-term memory (LSTM) neural network. The methods show how actual climate factors impact on solar irradiation, and the possibility of estimating one year local solar irradiance using machine learning methodologies with four different algorithms. This study conducted readily accessible local weather data including temperature, wind velocity and direction, air pressure, the amount of total cloud cover, the amount of middle and low-layer cloud cover, and humidity. The results show that the artificial neural network (ANN) models provided more close information on solar irradiance than the conventional techniques (linear regression and SVM). Between the two ANN models, the LSTM model achieved better performance, improving accuracy by 31.7% compared to the MLNN model. Impact factor analysis also revealed that temperature and the amount of total cloud cover are the dominant factors affecting solar irradiance, and the amount of middle and low-layer cloud cover is also an important factor. The results from this work demonstrate that ANN models, especially ones based on LSTM, can provide accurate information of local solar irradiance using weather data without installing and maintaining on-site solar irradiance sensors.

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“…However, many databases, accessed for retrieving the aerosol parameters required for running a clear sky solar irradiance model with global coverage, store the aerosol parameters with monthly to yearly sampling [8]. Solar irradiance estimated using monthly-averaged aerosol optical depth (AOD) was found biased compared to solar irradiance estimated using daily AOD [9].…”

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confidence: 99%

Short-Term PV Power Forecasting Based on Sky Imagery. A Case Study at the West University of Timisoara

Dughir

Sabadus

Stefu

et al. 2022

Annals of West University of Timisoara - Physics

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This study deals with the performance of PV2-state model in intra-hour forecasting of photovoltaic (PV) power. The PV2-state model links an empirical model for estimating the PV power delivered by a PV system under clear-sky with a model for forecasting the relative position of the Sun and clouds. Sunshine number (SSN), a binary quantifier showing if the Sun shines or not, is used as a measure for the Sun position with respect to clouds. A physics-based approach to intra-hour forecasting, processing cloud field information from an all-sky imager, is applied to predict SSN. The quality of SSN prediction conditions the overall quality of PV2-state forecasts. The PV2-state performance was evaluated against a challenging database (high variability in the state-of-the-sky, thin cloud cover, broken cloud field, isolated passing clouds) comprising radiometric data and sky-images collected on the Solar Platform of the West University of Timisoara, Romania. The investigation was performed from two perspectives: general model accuracy and, as a novelty, identification of characteristic elements in the state-of-the-sky which fault the SSN prediction. The outcome of such analysis represents the basis of further research aiming to increase the performance in PV power forecasting.

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Bias induced by the AOD representation time scale in long-term solar radiation calculations. Part 2: Impact on long-term solar irradiance predictions

Cited by 13 publications

References 35 publications

An investigation on the attenuation effect of air pollution on regional solar radiation

An investigation on the attenuation effect of air pollution on regional solar radiation

Investigation of Applicability of Impact Factors to Estimate Solar Irradiance: Comparative Analysis Using Machine Learning Algorithms

Short-Term PV Power Forecasting Based on Sky Imagery. A Case Study at the West University of Timisoara

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