Short-term solar radiation forecasting by advecting and diffusing MSG cloud index

Arbizu‐Barrena, Clara; Ruiz‐Arias, José A.; Rodríguez-Benítez, Francisco J.; Pozo-Vázquez, D.; Tovar‐Pescador, J.

doi:10.1016/j.solener.2017.07.045

Cited by 74 publications

(45 citation statements)

References 54 publications

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“…All these changes have been incorporated in a specific augmentation of the advanced research version of the WRF Model [35] designed for solar energy predictions known as WRF-Solar [36]. WRF-Solar has been extensively tested in the USA [36][37][38][39][40][41] and other countries, such as Spain [42], Singapore [43], Kuwait [44], and Saudi Arabia [45]. Most studies have reported significant improvements in solar irradiance predictions under different sky conditions with WRF-Solar in comparison to standard WRF simulations.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Simulated Solar Irradiance on Days of High Intermittency Using WRF-Solar

Prasad

Kay

2020

Energies

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Improvements in the short-term predictability of irradiance in numerical weather prediction models can assist grid operators in managing intermittent solar-generated electricity. In this study, the performance of the Weather Research and Forecasting (WRF) model when simulating different components of solar irradiance was tested under days of high intermittency at Mildura, a site located on the border of New South Wales and Victoria, Australia. Initially, four intermittent and clear case days were chosen, later extending to a full year study in 2005. A specific configuration and augmentation of the WRF model (version 3.6.1) designed for solar energy applications (WRF-Solar) with an optimum physics ensemble derived from literature over Australia was used to simulate solar irradiance with four nested domains nudged to ERA-Interim boundary conditions at grid resolutions (45, 15, 5, and 1.7 km) centred over Mildura. The Bureau of Meteorology (BOM) station dataset available at minute timescales and hourly derived satellite irradiance products were used to validate the simulated products. Results showed that on days of high intermittency, simulated solar irradiance at finer resolution was affected by errors in simulated humidity and winds (speed and direction) affecting clouds and circulation, but the latter improves at coarser resolutions; this is most likely from reduced displacement errors in clouds.

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Section: Introductionmentioning

confidence: 99%

Assessment of Simulated Solar Irradiance on Days of High Intermittency Using WRF-Solar

Prasad

Kay

2020

Energies

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“…For days-ahead predictions, numerical weather prediction (NWP) models have mostly been used. For minutes and hours (0-4 h) ahead forecasts, ground-based cloud images and satellite data, combined with an artificial neural network (ANN) or statistical methods, are most commonly used [8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Nowcasting of Surface Solar Irradiance Using FengYun-4 Satellite Observations over China

Yang

Gao

et al. 2019

Remote Sensing

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The accurate prediction of surface solar irradiance is of great significance for the generation of photovoltaic power. Surface solar irradiance is affected by many random mutation factors, which means that there are great challenges faced in short-term prediction. In Northwest China, there are abundant solar energy resources and large desert areas, which have broad prospects for the development of photovoltaic (PV) systems. For the desert areas in Northwest China, where meteorological stations are scarce, satellite remote sensing data are extremely precious exploration data. In this paper, we present a model using FY-4A satellite images to forecast (up to 15–180 min ahead) global horizontal solar irradiance (GHI), at a 15 min temporal resolution in desert areas under different sky conditions, and compare it with the persistence model (SP). The spatial resolution of the FY-4A satellite images we used was 1 km × 1 km. Particle image velocimetry (PIV) was used to derive the cloud motion vector (CMV) field from the satellite cloud images. The accuracy of the forecast model was evaluated by the ground observed GHI data. The results showed that the normalized root mean square error (nRMSE) ranged from 18.9% to 21.6% and the normalized mean bias error (nMBE) ranged from 3.2% to 4.9% for time horizons from 15 to 180 min under all sky conditions. Compared with the SP model, the nRMSE value was reduced by about 6%, 8%, and 14% with the time horizons of 60, 120, and 180 min, respectively.

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“…Short-term forecast of cloud cover still poses a challenge to the scientific community, due to the inherent complexity and non-linearity of cloud motion in atmosphere [1][2][3][4][5][6][7]. This topic is of relevance to many fields, including solar energy production [4,[8][9][10][11][12], since the presence of clouds has a significant impact on the stability and energy production of solar plants, causing dangerous fluctuations and of each nowcasting variant previously discussed, and use the most accurate one for predicting solar irradiance maps with AMESIS; the results are then evaluated against the AMESIS-based MSG measurements and the WRF-based irradiance forecast.…”

Section: Introductionmentioning

confidence: 99%

Nowcasting Surface Solar Irradiance with AMESIS via Motion Vector Fields of MSG-SEVIRI Data

et al. 2018

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Abstract:In this study, we compare different nowcasting techniques based upon the calculation of motion vector fields derived from spectral channels of Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager (MSG-SEVIRI). The outputs of the nowcasting techniques are used as inputs to the Advanced Model for Estimation of Surface solar Irradiance from Satellite (AMESIS), for predicting surface solar irradiance up to 2 h in advance. In particular, the first part of the methodology consists in projecting the time evolution of each MSG-SEVIRI channel (for every pixel in the spatial domain) through extrapolation of a displacement vector field obtained by matching similar patterns within two successive MSG-SEVIRI data images. Different ways to implement the above method result in substantial differences in the predicted trajectory, leading to different performances depending on the time interval of interest. All the nowcasting techniques considered here systematically outperform the simple persistence method for all MSG-SEVIRI channels and for each case study used in this work; importantly, this occurs across the entire 2 h period of the forecast. In the second part of the algorithm, the predicted irradiance maps computed with AMESIS from the forecasted radiances, are shown to be in good agreement with irradiances derived from MSG measured radiances and improve on numerical weather model predictions, thus providing a feasible alternative for nowcasting surface solar radiation. The results show that the mean values for correlation, bias, and root mean square error vary across the time interval, ranging between 0.94, −1 W/m 2 , 61 W/m 2 after 15 min, and 0.73, −18 W/m 2 , 147 W/m 2 after 2 h, respectively.

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Short-term solar radiation forecasting by advecting and diffusing MSG cloud index

Cited by 74 publications

References 54 publications

Assessment of Simulated Solar Irradiance on Days of High Intermittency Using WRF-Solar

Assessment of Simulated Solar Irradiance on Days of High Intermittency Using WRF-Solar

Nowcasting of Surface Solar Irradiance Using FengYun-4 Satellite Observations over China

Nowcasting Surface Solar Irradiance with AMESIS via Motion Vector Fields of MSG-SEVIRI Data

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