Contribution of ordinal variables to short-term global solar irradiation forecasting for sites with low variabilities

2021 ◽  
Author(s):  
Kacem Gairaa ◽  
Cyril Voyant ◽  
Gilles Notton ◽  
Saïd Benkaciali ◽  
Mawloud Guermoui
Keyword(s):  
Solar Irradiation ◽  
Short Term ◽  
Ordinal Variables ◽  
Global Solar Irradiation

scholarly journals Comparison Between Measurements and WRF Numerical Simulation of Global Solar Irradiation in Romania

2013 ◽  
Vol 57 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Dragos Isvoranu ◽  
Viorel Badescu
Keyword(s):  
Numerical Simulation ◽  
Preliminary Analysis ◽  
Measured Data ◽  
Solar Irradiation ◽  
Short Term ◽  
Data Accessibility ◽  
Power Stations ◽  
Photovoltaic Power ◽  
Global Solar Irradiation ◽  
Global Irradiation

Abstract The paper presents a comparative analysis between the surface global irradiation measured for Romania and the predicted irradiation obtained by numerical simulation. The measured data came from the Romanian National meteorological Administration. Based on a preliminary analysis that took into account several criteria among which, performance, cost, popularity and meteorological and satellite data accessibility we concluded that a combination GFS-WRF(NMM) or GFS-WRF(ARW) is most suitable for short term global solar irradiation forecasting in order to assess the performance of the photovoltaic power stations (Badescu and Dumitrescu, 2012, [1], Martin et al., 2011, [2]).

scholarly journals Preliminary Wrf-Arw Model Analysis of Global Solar Irradiation Forecasting

2014 ◽  
Vol 10 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Dragos Isvoranu ◽  
Viorel Badescu
Keyword(s):  
Solar Irradiance ◽  
Solar Irradiation ◽  
Short Term ◽  
Simulation Data ◽  
Power Stations ◽  
Global Solar Irradiation ◽  
Time Period ◽  
Forecasting Performance ◽  
Arw Model ◽  
Best Fit

Abstract The purpose of this research is focused on the evaluation of short term global solar irradiation forecasting performance in order to assess the outcome of photovoltaic power stations. The paper presents a comparative analysis between the predicted irradiation obtained by numerical simulation and measurements. The simulation data is obtained from WRF-ARW model (Weather Research Forecasting-Advanced Research WRF), whose initial and boundary conditions are provided by the global forecasting model GFS. Taking into account the complexity of options for the physics models provided with WRF, we embarked upon a parametric analysis of the simulated solar irradiance. This complex task provides a better insight among the coupling of various physics options and enables us to find the best fit with the measured data for a specified site and time period. The present preliminary analysis shows that the accuracy of the computed global solar irradiance can be improved by choosing the appropriate built-in physics models. A combination of physics models providing the best results has been identified.

scholarly journals Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3030
Author(s):  
Simon Liebermann ◽  
Jung-Sup Um ◽  
YoungSeok Hwang ◽  
Stephan Schlüter
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Renewable Energy Sources ◽  
Weather Conditions ◽  
Solar Irradiation ◽  
Weather Data ◽  
Short Term ◽  
Climate Conditions ◽  
Lstm Network ◽  
The Time Domain

Due to the globally increasing share of renewable energy sources like wind and solar power, precise forecasts for weather data are becoming more and more important. To compute such forecasts numerous authors apply neural networks (NN), whereby models became ever more complex recently. Using solar irradiation as an example, we verify if this additional complexity is required in terms of forecasting precision. Different NN models, namely the long-short term (LSTM) neural network, a convolutional neural network (CNN), and combinations of both are benchmarked against each other. The naive forecast is included as a baseline. Various locations across Europe are tested to analyze the models’ performance under different climate conditions. Forecasts up to 24 h in advance are generated and compared using different goodness of fit (GoF) measures. Besides, errors are analyzed in the time domain. As expected, the error of all models increases with rising forecasting horizon. Over all test stations it shows that combining an LSTM network with a CNN yields the best performance. However, regarding the chosen GoF measures, differences to the alternative approaches are fairly small. The hybrid model’s advantage lies not in the improved GoF but in its versatility: contrary to an LSTM or a CNN, it produces good results under all tested weather conditions.

scholarly journals Mapping and Estimation of Monthly Global Solar Irradiation in Different Zones in Souss-Massa Area, Morocco, Using Artificial Neural Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-19 ◽  
Author(s):  
O. Nait Mensour ◽  
S. Bouaddi ◽  
B. Abnay ◽  
B. Hlimi ◽  
A. Ihlal
Keyword(s):  
Solar Energy ◽  
Solar Irradiation ◽  
Kriging Interpolation ◽  
Solar Pv ◽  
Energy Potential ◽  
Global Solar Irradiation ◽  
Interpolation Technique ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Solar radiation data play an important role in solar energy research. However, in regions where the meteorological stations providing these data are unavailable, strong mapping and estimation models are needed. For this reason, we have developed a model based on artificial neural network (ANN) with a multilayer perceptron (MLP) technique to estimate the monthly average global solar irradiation of the Souss-Massa area (located in the southwest of Morocco). In this study, we have used a large database provided by NASA geosatellite database during the period from 1996 to 2005. After testing several models, we concluded that the best model has 25 nodes in the hidden layer and results in a minimum root mean square error (RMSE) equal to 0.234. Furthermore, almost a perfect correlation coefficient R=0.988 was found between measured and estimated values. This developed model was used to map the monthly solar energy potential of the Souss-Massa area during a year as estimated by the ANN and designed with the Kriging interpolation technique. By comparing the annual average solar irradiation between three selected sites in Souss-Massa, as estimated by our model, and six European locations where large solar PV plants are deployed, it is apparent that the Souss-Massa area is blessed with higher solar potential.

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