During the last decade, many research has been introduced to forecast power energy such as solar, wind and wave. However, I would like to know the best methodologies used in this study.
The climatic factors in the coastal areas are cogent in planning a stable and functional solar farm. 3D simulations relating the surface temperature, sunshine hour,to see the effect of minute changes of other meteorological parameters the solar irradiance . This enabled the day-to-day solar radiation monitoring with the primary objective to examine the best technique for maximum power generation via solar option in coastal locations. The month of January had the highest turbulent features, showing the influence of weather and the poorest solar radiance due to low sunshine hour. Twenty-year weather parameters in the research area were simulated to express the systematic influence of weather of PV performance. A theoretical solar farm was illustrated to generate stable power supply with emphasis on the longevity of the PV module proposed by introducing an electronic concentrator pillar (CP). The pictorial and operational model of the solar farm was adequately explained.
@ali alwaeli
Dear Ali thank you for your reply.
However, can share with me an example of data bases for eventually published. I have started with this type of topic. The obtained results is promising to have a great attention.
Best regards.
Solar power forecasting involves knowledge of the Sun´s path, the atmosphere's condition, the scattering processes and the characteristics of a solar energy plant which utilizes the Sun's energy to create solar power. Solar photovoltaic systems transform solar energy into electric power. The power output depends on the incoming radiation and on the solar panel characteristics. Photovoltaic power production is increasing nowadays. Forecast information is essential for an efficient use, the management of the electricity grid and for solar energy trading. Common solar forecasting method include stochastic learning method, local and remote sensing method, and hybrid method (Chu et al. 2016).
One important question for this type of research is how to compare alternative forecasting methods.
Let me recommend this paper:
Chapter Forecast Error Measures: Critical Review and Practical Recommendations
On page 8 you can find a step-by-step algorithm of how to compare alternative forecasting procedures.
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