Coupling satellite images with surface measurements of bright sunshine hours to estimate daily solar irradiation on horizontal surface

Rusen, Selmin Ener; Hammer, Annette; Akinoglu, Bülent G.

doi:10.1016/j.renene.2012.12.019

Cited by 23 publications

(8 citation statements)

References 13 publications

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“…Such approaches were carried out for the daily values and very satisfactory and promising results were obtained. [7][8][9]30 One of these approaches by considering a physical base yielded an expression of the form as in Eq. (14).…”

Section: Discussion and Comparisonsmentioning

confidence: 99%

“…Extended searches on this subject are conducted and published for the daily values of global solar irradiation on horizontal surface. [7][8][9] Satellite based models such as HELIOSAT are reliable hourly based estimation methods which are firstly developed by Cano et al 6 and used later by many researchers. 3,5,23 Such methods start by calculating the cloud index n and using it directly in the estimations.…”

Section: A Angstrom Type Modelsmentioning

confidence: 99%

“…For the daily correlations, one may use the daily average values of cloud index n avg and recently these daily values were used to construct models for daily global solar irradiation values on a horizontal surface. 7,9,22 C. Correlations between n and s/S and its use in Angstrom type expression Olseth and Skartveit 26 proposed that satellite derived cloud index n and surface measured bright sunshine hours should correlate. However, they do not present a correlation equation between these two measured quantities.…”

Section: A Angstrom Type Modelsmentioning

confidence: 99%

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Combining the satellite imagery with bright sunshine hours: A review

Akinoglu

Rusen

2013

Journal of Renewable and Sustainable Energy

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Present article is a review of recent researches on linking the satellite image data to surface measured bright sunshine hours. It first gives some basics on estimation procedures of the daily global solar irradiation on horizontal surface with the daily bright sunshine hours. Second, a satellite based model is presented briefly. Then, two recent combined models are reviewed and compared with the conventional models. The results on using one of the presented methods are discussed and recommended, for a couple of various cases which are classified by available data of the site of interest. It is concluded that the use of one of the combined model gives one of the best estimation performance for the daily global solar irradiation at the Earth's surface, whenever both of the data of bright sunshine and cloud index are available. However, satellite based model HELIOSAT also performs with high accuracy except for a number of days of one of the data sets. Hence, another conclusion is that, for the daily estimations, the use of HELIOSAT type satellite based models should be preferred, instead of using an Angstr€ om-Prescott type correlation of a location at a far point and/or having a different climate. Finally, a quadratic correlation which was tested for too many locations all over the world also performs quite accurately. Therefore, if only the bright sunshine hour data is available for a site, use of this quadratic correlation should be preferred. V C 2013 AIP Publishing LLC. [http://dx.

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Section: Discussion and Comparisonsmentioning

confidence: 99%

Section: A Angstrom Type Modelsmentioning

confidence: 99%

Section: A Angstrom Type Modelsmentioning

confidence: 99%

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Combining the satellite imagery with bright sunshine hours: A review

Akinoglu

Rusen

2013

Journal of Renewable and Sustainable Energy

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“…Most of the existing literature describes short-term forecasting research with an hourly cycle. There are few reports on the ultra-short-term prediction of PV power generation [4][5][6]. In addition, in the previous research, PV power prediction methods mainly include the following: physical methods, statistical methods, machine learning methods, and hybrid integration methods.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Forecasting Method for Solar Output Power Based on Variational Mode Decomposition, Deep Belief Networks and Auto-Regressive Moving Average

et al. 2018

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Due to the existing large-scale grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economical operation of electric power systems. In this study, a hybrid short-term forecasting method based on the Variational Mode Decomposition (VMD) technique, the Deep Belief Network (DBN) and the Auto-Regressive Moving Average Model (ARMA) is proposed to deal with the problem of forecasting accuracy. The DBN model combines a forward unsupervised greedy layer-by-layer training algorithm with a reverse Back-Projection (BP) fine-tuning algorithm, making full use of feature extraction advantages of the deep architecture and showing good performance in generalized predictive analysis. To better analyze the time series of historical data, VMD decomposes time series data into an ensemble of components with different frequencies; this improves the shortcomings of decomposition from Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) processes. Classification is achieved via the spectrum characteristics of modal components, the high-frequency Intrinsic Mode Functions (IMFs) components are predicted using the DBN, and the low-frequency IMFs components are predicted using the ARMA. Eventually, the forecasting result is generated by reconstructing the predicted component values. To demonstrate the effectiveness of the proposed method, it is tested based on the practical information of PV power generation data from a real case study in Yunnan. The proposed approach is compared, respectively, with the single prediction models and the decomposition-combined prediction models. The evaluation of the forecasting performance is carried out with the normalized absolute average error, normalized root-mean-square error and Hill inequality coefficient; the results are subsequently compared with real-world scenarios. The proposed approach outperforms the single prediction models and the combined forecasting methods, demonstrating its favorable accuracy and reliability.

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“…The radiative transferring method, taking into account several atmospheric processes such as Rayleigh scattering, water vapour and ozone absorption and aerosol contents, is more efficient in predicting the solar radiation, for example, Gueymard () predicted the cloudless shortwave solar spectra incident on horizontal, tilted and tracking surfaces, respectively; Halthore et al () compared different radiative transfer codes for G estimations. Some attempts have also been made to derive G from satellite observations considering the effects of water vapour, cloud, ozone and aerosols in recent decades (Dubayah and Loechel, ; Chen et al , ), for example, Hay and Hanson () determined the solar irradiance at the ocean surface during GATE using satellite‐based methodology; Janjai et al () developed an model for calculating hourly G from satellite data in the tropics; Rusen et al () coupled satellite images with surface measurements of bright sunshine hours to estimate daily solar irradiation on horizontal surface, but the remote sensing method is also affected by the cloud, aerosols and the passage interval of the corresponding satellites, the model accuracy may not be so good over a small region (field scale) (Zarzalejo et al , ; Li et al , ). Above methodologies have shown relatively low performances when forecasting solar radiation in long‐term basis, and they are not suitable when there are some missing data in the database (Yadav and Chandel, ; Kumar et al , ; Manzano et al , ).…”

Section: Introductionmentioning

confidence: 99%

Prediction of solar radiation in China using different adaptive neuro‐fuzzy methods and M5 model tree

Wang

Kisi

Zounemat‐Kermani

et al. 2016

Intl Journal of Climatology

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Solar radiation is one of the major factors for agricultural, meteorological and ecological applications. In this study, two different optimized adaptive neuro-fuzzy inference systems (ANFIS), ANFIS with grid partition (ANFIS-GP) and ANFIS with subtractive clustering (ANFIS-SC), and M5Tree (M5Tree) methods are proposed for modelling daily global solar radiation (G). Daily meteorological variables at 21 stations in China are used for training and testing the applied models, which is evaluated through root mean square errors (RMSE), mean absolute errors (MAE) and determination coefficient (R 2 ). Above models will be compared with a calibrated empirical Ångström model and the results indicate that the ANFIS models provide better accuracy than the M5Tree and empirical method, for example, the RMSE values for ANFIS-SC, ANFIS-GP, M5Tree and the Ångström model range 2.10-3.08, 2.07-3.08, 2.79-3.87 and 2.54-3.69 MJ m −2 day −1 , respectively. The model performances also show some differences at different stations for each model, for example, the ANFIS models produce the most accurate estimations at station 58238, while M5Tree brings the best accuracy at the station 51777. Meanwhile, the models underestimate high radiation values for some stations, which may due to the differences in training and testing data ranges and distribution of the stations. Finally, the reasons for the differences in model performance are investigated in detail.

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Coupling satellite images with surface measurements of bright sunshine hours to estimate daily solar irradiation on horizontal surface

Cited by 23 publications

References 13 publications

Combining the satellite imagery with bright sunshine hours: A review

Combining the satellite imagery with bright sunshine hours: A review

A Hybrid Forecasting Method for Solar Output Power Based on Variational Mode Decomposition, Deep Belief Networks and Auto-Regressive Moving Average

Prediction of solar radiation in China using different adaptive neuro‐fuzzy methods and M5 model tree

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