The influence of dust storms on solar radiation data, aerosol properties and meteorological variables in Central Arabian Peninsula

AbstractDue to the importance of generating energy sustainably, with the Sun being a large solar power plant for the Earth, we study the cross-correlations between the main meteorological variables (global solar radiation, air temperature, and relative air humidity) from a global cross-correlation perspective to efficiently capture solar energy. This is done initially between pairs of these variables, with the Detrended Cross-Correlation Coefficient, ρDCCA, and subsequently with the recently developed Multiple Detrended Cross-Correlation Coefficient, $${\boldsymbol{DM}}{{\boldsymbol{C}}}_{{\bf{x}}}^{{\bf{2}}}$$DMCx2. We use the hourly data from three meteorological stations of the Brazilian Institute of Meteorology located in the state of Bahia (Brazil). Initially, with the original data, we set up a color map for each variable to show the time dynamics. After, ρDCCA was calculated, thus obtaining a positive value between the global solar radiation and air temperature, and a negative value between the global solar radiation and air relative humidity, for all time scales. Finally, for the first time, was applied $${\boldsymbol{DM}}{{\boldsymbol{C}}}_{{\bf{x}}}^{{\bf{2}}}$$DMCx2 to analyze cross-correlations between three meteorological variables at the same time. On taking the global radiation as the dependent variable, and assuming that $${\boldsymbol{DM}}{{\boldsymbol{C}}}_{{\bf{x}}}^{{\bf{2}}}={\bf{1}}$$DMCx2=1 (which varies from 0 to 1) is the ideal value for the capture of solar energy, our analysis finds some patterns (differences) involving these meteorological stations with a high intensity of annual solar radiation.

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Deep learning based solar radiation micro forecast by fusion of infrared cloud images and radiation data

Applied Energy ◽

10.1016/j.apenergy.2021.117014 ◽

2021 ◽

Vol 294 ◽

pp. 117014

Author(s):

Meenu Ajith ◽

Manel Martínez-Ramón

Keyword(s):

Deep Learning ◽

Solar Radiation ◽

Radiation Data

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Stochastic modeling and forecasting of solar radiation data

Solar Energy ◽

10.1016/0038-092x(77)90041-x ◽

1977 ◽

Vol 19 (6) ◽

pp. 755-757 ◽

Cited By ~ 68

Author(s):

T.N. Goh ◽

K.J. Tan

Keyword(s):

Solar Radiation ◽

Stochastic Modeling ◽

Radiation Data ◽

Modeling And Forecasting

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Generation of solar radiation data in unmeasurable areas for photovoltaic power station planning

2014 IEEE PES General Meeting | Conference & Exposition ◽

10.1109/pesgm.2014.6939206 ◽

2014 ◽

Cited By ~ 1

Author(s):

Chenxing Yang ◽

Qingshan Xu ◽

Xiaohui Xu ◽

Pingliang Zeng ◽

Xiaodong Yuan

Keyword(s):

Solar Radiation ◽

Power Station ◽

Radiation Data ◽

Photovoltaic Power

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Assessment of solar radiation data quality in typical meteorological years and its influence on the building performance simulation

Energy and Buildings ◽

10.1016/j.enbuild.2021.111251 ◽

2021 ◽

pp. 111251

Author(s):

Facundo Bre ◽

Rayner Maurício e Silva Machado ◽

Linda K. Lawrie ◽

Drury B. Crawley ◽

Roberto Lamberts

Keyword(s):

Solar Radiation ◽

Data Quality ◽

Building Performance ◽

Performance Simulation ◽

Building Performance Simulation ◽

Radiation Data

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06/00217 Quality control of solar radiation data: Presentstatus and proposed new approaches

Fuel and Energy Abstracts ◽

10.1016/s0140-6701(06)80217-1 ◽

2006 ◽

Vol 47 (1) ◽

pp. 31

Keyword(s):

Quality Control ◽

Solar Radiation ◽

Radiation Data ◽

New Approaches

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Comparative Study of Ground Measured, Satellite-Derived, and Estimated Global Solar Radiation Data in Nigeria

Journal of Solar Energy ◽

10.1155/2016/8197389 ◽

2016 ◽

Vol 2016 ◽

pp. 1-7 ◽

Cited By ~ 7

Author(s):

Boluwaji M. Olomiyesan ◽

Onyedi D. Oyedum

Keyword(s):

Solar Radiation ◽

Comparative Study ◽

Global Radiation ◽

Statistical Error ◽

Global Solar Radiation ◽

Coefficient Of Determination ◽

Mean Bias Error ◽

Percentage Error ◽

Radiation Data ◽

Proposed Model

In this study, the performance of three global solar radiation models and the accuracy of global solar radiation data derived from three sources were compared. Twenty-two years (1984–2005) of surface meteorological data consisting of monthly mean daily sunshine duration, minimum and maximum temperatures, and global solar radiation collected from the Nigerian Meteorological (NIMET) Agency, Oshodi, Lagos, and the National Aeronautics Space Agency (NASA) for three locations in North-Western region of Nigeria were used. A new model incorporating Garcia model into Angstrom-Prescott model was proposed for estimating global radiation in Nigeria. The performances of the models used were determined by using mean bias error (MBE), mean percentage error (MPE), root mean square error (RMSE), and coefficient of determination (R2). Based on the statistical error indices, the proposed model was found to have the best accuracy with the least RMSE values (0.376 for Sokoto, 0.463 for Kaduna, and 0.449 for Kano) and highest coefficient of determination, R2 values of 0.922, 0.938, and 0.961 for Sokoto, Kano, and Kaduna, respectively. Also, the comparative study result indicates that the estimated global radiation from the proposed model has a better error range and fits the ground measured data better than the satellite-derived data.

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