Modeling water vapor impacts on the solar irradiance reaching the receiver of a solar tower plant by means of artificial neural networks

López, Gabriel; Gueymard, Christian A.; Bosch, J.L.; Rapp-Arrarás, Ígor; Alonso-Montesinos, Joaquín; Pulido‐Calvo, Inmaculada; Ballestrín, Jesús; Polo, Jesús; Barbero, Javier

doi:10.1016/j.solener.2018.04.023

Cited by 30 publications

(10 citation statements)

References 14 publications

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“…In addition, although the WV channel did not show a clear trend with solar irradiance in Figure 12f, this channel had a considerable impact on the solar irradiance because the model accuracy became reduced when training was conducted without the WV channel. Actually, water vapor is generally known to influence solar irradiance on the ground, especially in cloudless conditions [49]. Figure 12.…”

Section: Solar Irradiance Mapmentioning

confidence: 99%

Estimation and Mapping of Solar Irradiance for Korea by Using COMS MI Satellite Images and an Artificial Neural Network Model

Koo

Kim

et al. 2020

Energies

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The power capacity of solar photovoltaics (PVs) in Korea has grown dramatically in recent years, and an accurate estimation of solar resources is crucial for the efficient management of these solar PV systems. Since the number of solar irradiance measurement sites is insufficient for Korea, satellite images can be useful sources for estimating solar irradiance over a wide area of Korea. In this study, an artificial neural network (ANN) model was constructed to calculate hourly global horizontal solar irradiance (GHI) from Korea Communication, Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) images. Solar position variables and five COMS MI channels were used as inputs for the ANN model. The basic ANN model was determined to have a window size of five for the input satellite images and two hidden layers, with 30 nodes on each hidden layer. After these ANN parameters were determined, the temporal and spatial applicability of the ANN model for solar irradiance mapping was validated. The final ANN ensemble model, which calculated the hourly GHI from 10 independent ANN models, exhibited a correlation coefficient (R) of 0.975 and root mean square error (RMSE) of 54.44 W/m² (12.93%), which were better results than for other remote-sensing based works for Korea. Finally, GHI maps for Korea were generated using the final ANN ensemble model. This COMS-based ANN model can contribute to the efficient estimation of solar resources and the improvement of the operational efficiency of solar PV systems for Korea.

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Section: Solar Irradiance Mapmentioning

confidence: 99%

Estimation and Mapping of Solar Irradiance for Korea by Using COMS MI Satellite Images and an Artificial Neural Network Model

Koo

Kim

et al. 2020

Energies

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“…All the weighted inputs are summed, and a neuron activation function is activated, such that it generates neuronal learning; when passing to the last neuronal layer, the output data are obtained -in our case, the (y est ) results. A representation of the artificial neural networks model is described, as in Equation (4), where N is the total number of neurons [37,38].…”

Section: Estimation Of the Dni Attenuation Factor Using Neural Networkmentioning

confidence: 99%

“…The Levenberg-Marquardt algorithm was used to train the neurons since it has a faster MSE convergence speed, facilitating the computation time [37,38]. To maintain the same proportion as in the previous models, we used 300 registers to train the neural network; 50 registers were occupied for validation and 50 for testing (400 registers in total).…”

Section: Estimation Of the Dni Attenuation Factor Using Neural Networkmentioning

confidence: 99%

Attenuation Factor Estimation of Direct Normal Irradiance Combining Sky Camera Images and Mathematical Models in an Inter-Tropical Area

et al. 2020

Self Cite

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Nowadays, it is of great interest to know and forecast the solar energy resource that will be constantly available in order to optimize its use. The generation of electrical energy using CSP (concentrated solar power) plants is mostly affected by atmospheric changes. Therefore, forecasting solar irradiance is essential for planning a plant's operation. Solar irradiance/atmospheric (clouds) interaction studies using satellite and sky images can help to prepare plant operators for solar surface irradiance fluctuations. In this work, we present three methodologies that allow us to estimate direct normal irradiance (DNI). The study was carried out at the Solar Irradiance Observatory (SIO) at the Geophysics Institute (UNAM) in Mexico City using corresponding images obtained with a sky camera and starting from a clear sky model. The multiple linear regression and polynomial regression models as well as the neural networks model designed in the present study, were structured to work under all sky conditions (cloudy, partly cloudy and cloudless), obtaining estimation results with 82% certainty for all sky types. and overcast skies over the short-term (from 1 to 180 min) using a sky camera, where the average nRMSE values obtained were 24. 36%, 20.9% and 19.17%, respectively [5]. G. Reikard calculated the solar irradiance over time horizons of 60, 30, 15, and 5 min, implementing Autoregressive Integrated Moving Average (ARIMA) with errors between 20% and 90% [6]. Solar irradiance forecasting applied to photovoltaic energy production was implemented using the Smart Persistence algorithm in Machine Learning techniques, achieving an nRMSE of 25% on the best panels over short horizons, and 33% over a 6 h horizon [7].An analysis of energy forecasting in solar-tower plants combining a short-term solar irradiation forecasting scheme with a solar-tower plant model, the System Advisor Model (SAM), was used to simulate the behavior of the Gemasolar and Crescent Dunes plants. The findings showed that the best results appeared for the 90-min horizon, where the annual forecasting energy yield for Gemasolar was 97.34 GWh year while for Crescent Dunes it was 392.57 GWh year [8]. Similarly, cloud abundance forecasting has been studied for timescales of between (1-180 min), resulting in short-term forecasting (of less than one hour) and medium-term forecasting (up to 3 h), which was proven to have an 80% success rate-indeed, it was so successful that an application (portal) tool was developed that helps to increase power plant production [9,10].Recent studies have presented a method for the probabilistic forecasting of solar irradiance based on the joint Probability Distribution Function (PDF) of irradiance predicted using the Numerical Weather Prediction (NWP) and the irradiance observed; these are based on models of meteorological processes such as atmospheric dynamics, cloud formation and radiative transfer processes [11]. H. Yang and B. Kurtz estimated direct solar irradiance over the short and medium term for different sky conditions ...

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“…The Levenberg-Marquardt algorithm was used due to its reduced time required for convergence, and its results are better than others such as the Bayesian Regularization and Scaled Conjugate Gradient in the case of modelling GHI [54,[75][76][77]. However, another reason is that we compared the initial results of Levenberg-Marquardt to the other two training algorithms and its results were better than they were.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks

et al. 2019

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More accurate data of hourly Global Horizontal Irradiance (GHI) are required in the field of solar energy in areas with limited ground measurements. The aim of the research was to obtain more precise and accurate hourly GHI by using new input from Satellite-Derived Datasets (SDDs) with new input combinations of clear sky (Cs) and top-of-atmosphere (TOA) irradiance on the horizontal surface and with observed climate variables, namely Sunshine Duration (SD), Air Temperature (AT), Relative Humidity (RH) and Wind Speed (WS). The variables were placed in ten different sets as models in an artificial neural network with the Levenberg–Marquardt training algorithm to obtain results from training, validation and test data. It was applied at two station types in northeast Iraq. The test data results with observed input variables (correlation coefficient (r) = 0.755, Root Mean Square Error (RMSE) = 33.7% and bias = 0.3%) are improved with new input combinations for all variables (r = 0.983, RMSE = 9.5% and bias = 0.0%) at four automatic stations. Similarly, they improved at five tower stations with no recorded SD (from: r = 0.601, RMSE = 41% and bias = 0.7% to: r = 0.976, RMSE = 11.2% and bias = 0.0%). The estimation of hourly GHI is slightly enhanced by using the new inputs.

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Modeling water vapor impacts on the solar irradiance reaching the receiver of a solar tower plant by means of artificial neural networks

Cited by 30 publications

References 14 publications

Estimation and Mapping of Solar Irradiance for Korea by Using COMS MI Satellite Images and an Artificial Neural Network Model

Estimation and Mapping of Solar Irradiance for Korea by Using COMS MI Satellite Images and an Artificial Neural Network Model

Attenuation Factor Estimation of Direct Normal Irradiance Combining Sky Camera Images and Mathematical Models in an Inter-Tropical Area

Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks

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