研究目的
Investigating the forecasting of solar radiation to anticipate the injection of electrical power produced by a photovoltaic generator.
研究成果
The study demonstrates that coupling WRF with libRadtran provides better forecasting results for solar radiations, especially in situations with small and isolated clouds. The behavioral electrical model effectively computes the forecasted electrical power, showing good correlation with measured values. However, long-term validation is suggested for more objective criticism.
研究不足
The study notes that for very isolated and small-fractionated clouds, the forecasted global radiation may be randomized, impacting the forecasted electrical power. Additionally, the spatial resolution difference between measured and forecasted values can lead to discrepancies.
1：Experimental Design and Method Selection:
The study uses the Numerical Weather Prediction model WRF coupled with a radiative transfer model libRadtran to forecast solar radiations. A behavioral electrical power model is used to compute the electrical power produced by a photovoltaic generator.
2：Sample Selection and Data Sources:
The study focuses on a photovoltaic generator located at the Center of Development of Renewable Energy in Algiers, Algeria. MERRA-2 analysis data are used for validation.
3：List of Experimental Equipment and Materials:
WRF model (NMM core), libRadtran model (version 2.0.1), photovoltaic generator.
4：1), photovoltaic generator.
Experimental Procedures and Operational Workflow:
4. Experimental Procedures and Operational Workflow: WRF forecasts atmospheric parameters, which are input into libRadtran to calculate solar radiations. These radiations are then used to compute the electrical power via the behavioral model.
5：Data Analysis Methods:
Statistical validation using MERRA-2 data, correlation analysis, bias and RMSE calculations.
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