Recent research published in the International Journal of Renewable Energy Development has shed light on the performance of the Anwaralardh photovoltaic power generation plant in Jordan. This study, led by Suhaib Ibrahim Alma’asfa from the Mechanical Engineering Department at Al-Hussein Bin Talal University, utilized advanced modeling techniques—specifically Artificial Neural Networks (ANNs) and multiple linear regression (MLR)—to assess the plant’s productivity.
As global energy demands continue to rise due to population growth and industrialization, the shift towards renewable energy sources, particularly solar energy, is becoming increasingly vital. The Anwaralardh plant, with an operational capacity of 11 MW, serves as a case study to explore how effectively solar power can be harnessed in arid desert climates.
The research revealed that both ANN and MLR models can predict daily, monthly, and yearly power production with high accuracy. The ANN model performed notably well, achieving a coefficient of determination (R²) of 95.85% during training and 93.7% during validation, indicating a strong correlation between predicted and actual outputs. The MLR model also showed promising results, with an R² of 93.42%. The actual yearly output power was estimated at 24,399 MWh, closely aligning with the predictions from both models.
These findings are significant for commercial stakeholders in the renewable energy sector. The ability to accurately predict solar energy production can enhance operational efficiency and optimize investment strategies for solar power projects. As Alma’asfa noted, “This research showed valuable results in the monthly output power for solar cells at the Anwaralardh PV power system project, contributing to a better understanding of solar energy generation in arid desert climates.”
The implications of this study extend beyond Jordan, as countries in similar geographical regions can leverage these insights to improve their solar energy initiatives. The research highlights the potential for solar power plants to significantly contribute to achieving Sustainable Development Goal 7, which aims to ensure access to affordable, reliable, sustainable, and modern energy for all.
For investors and companies involved in solar technology and renewable energy, this research opens up opportunities for developing more efficient solar projects and enhancing predictive analytics capabilities. The findings could lead to more robust business models, ultimately fostering growth in the renewable energy market and supporting global sustainability efforts.
