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 The Importance of Distance between Photovoltaic Power Stations for Clear Accuracy of Short-Term Photovoltaic Power Forecasting

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Abdelhakim El hendouzi ◽  
Abdennaser Bourouhou ◽  
Omar Ansari
Keyword(s):  
Absolute Error ◽  
Skill Score ◽  
Weather Data ◽  
Short Term ◽  
Pv System ◽  
Pv Systems ◽  
Forecasting Models ◽  
Power Stations ◽  
Photovoltaic Power ◽  
Power Forecasting

The current research paper deals with the worldwide problem of photovoltaic (PV) power forecasting by this innovative contribution in short-term PV power forecasting time horizon based on classification methods and nonlinear autoregressive with exogenous input (NARX) neural network model. In the meantime, the weather data and PV installation parameters are collected through the data acquisition systems installed beside the three PV systems. At the same time, the PV systems are located in Morocco country, respectively, the 2 kWp PV installation placed at the Higher Normal School of Technical Education (ENSET) in Rabat city, the 3 kWp PV system set at Nouasseur Casablanca city, and the 60 kWp PV installation also based in Rabat city. The multisite modelling approach, meanwhile, is deployed for establishing the flawless short-term PV power forecasting models. As a result, the implementation of different models highlights their achievements in short-term PV power forecasting modelling. Consequently, the comparative study between the benchmarking model and the forecasting methods showed that the forecasting techniques used in this study outperform the smart persistence model not only in terms of normalized root mean square error (nRMSE) and normalized mean absolute error (nMAE) but also in terms of the skill score technique applied to assess the short-term PV power forecasting models.

An Anisotropic Model for Inclined Surface Solar Irradiation

1987 ◽  
Vol 201 (1) ◽  
pp. 11-20 ◽  
Author(s):  
G S Saluja ◽  
T Muneer
Keyword(s):  
United Kingdom ◽  
Measured Data ◽  
Solar Irradiation ◽  
Anisotropic Model ◽  
Short Term ◽  
The United Kingdom ◽  
Inclined Surfaces ◽  
Proposed Model ◽  
One Year ◽  
Inclined Surface

The validity of various models for predicting irradiance on inclined surfaces was investigated by employing one-year measured hourly irradiation data on horizontal and vertical surfaces for Easthampstead (51.4°N, 0.8°W) in the United Kingdom. This led to the development of an improved anisotropic model that distinguished between shaded and sunlit surfaces and between overcast and non-overcast conditions of the sunlit surfaces. The accuracy of the proposed model was compared with those of Klucher and Hay and the model was further tested against short-term measured data for four widely separated locations in the United Kingdom.

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.

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