Open-Source Climate Modeling for Solar and Wind Energy Estimation: Validation with SAM Data and Experimental Measurements

Climate modeling for solar and wind energy estimation involves predicting weather parameters like sunlight, wind speed, and atmospheric conditions to assess energy potential. The current study introduces reliable predictions which helps to optimize the placement and efficiency of solar panels and wind turbines. Continuous refinement of models enhances renewable energy planning and integration into power grids. This study assesses the performance of an open-source model in predicting environmental parameters, with a particular focus on bias as the primary evaluation metric. The predictions generated by the current code output are compared with observed data and results from the System Advisor Model (SAM), which utilizes Typical Meteorological Year (TMY) data for the Al-Baha region. The findings reveal that the current code output generally exhibits lower biases across most parameters compared to SAM, underscoring its reliability and adaptability. For temperature, the current code output shows a bias of -4.62 compared to SAM's -2.01, with both models underestimating. In the case of relative humidity, the current code output demonstrates a bias of 7.59, slightly higher than SAM's 7.14, indicating comparable alignment with actual data. Regarding pressure, the current code output exhibits a bias of 8.51, which is somewhat larger than SAM's 2.79, though both remain within acceptable accuracy. For wind speed, the bias of the current code output is 0.82, higher than SAM's 0.47, with both models slightly overestimating. In contrast, for wind direction, the current code output achieves a substantially smaller bias of 17.35 compared to SAM's 37.78. For direct normal irradiance (DNI), the Current Code Output has a bias of -9.11, reflecting a minor underestimation, whereas SAM significantly overestimates with a bias of 44.85. The open-source nature of the current code presents considerable potential for further refinement. Its customizable and adaptable framework allows for recalibration to specific datasets, fostering continuous improvements in accuracy. By addressing biases in temperature and pressure, the model could evolve into a highly reliable and precise tool for environmental parameter prediction. Its flexibility supports collaborative development, making it a robust and viable alternative to SAM's TMY-based data for modeling in the Al-Baha region.