Evaluation of energy extraction of PV systems affected by environmental factors under real outdoor conditions

• Published in: Theoretical and applied climatology Vol. 150; no. 1-2; pp. 715 - 729
• Main Authors: Hassan, Muhammed A., Bailek, Nadjem, Bouchouicha, Kada, Ibrahim, Abdelhameed, Jamil, Basharat, Kuriqi, Alban, Nwokolo, Samuel Chukwujindu, El-kenawy, El-Sayed M.
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01.10.2022; Springer; Springer Nature B.V
• Subjects: Air temperature; Alternative energy sources; Ambient temperature; Analysis; Aquatic Pollution; Arid climates; Atmospheric Protection/Air Quality Control/Air Pollution; Atmospheric Sciences; Case studies; Climate; Climate science; Climatic conditions; Climatology; Deserts; Earth and Environmental Science; Earth Sciences; Electric power production; Energy; Environmental factors; Irradiance; Original Paper; Performance enhancement; Photovoltaic cells; Photovoltaics; Power plants; Relative humidity; Renewable energy; Solar energy; Solar irradiance; Solar power plants; Waste Water Technology; Water Management; Water Pollution Control; Photovoltaic; Desert; Renewable energy; Clean energy; Power generation
• ISSN: 0177-798X; 1434-4483
• DOI: 10.1007/s00704-022-04166-6

The global agenda to increase the renewable energy share has driven many countries and entities to harness solar energy from solar photovoltaic (PV) systems. However, the power generation of PV systems is strongly affected by climate conditions. Therefore, the main objective of this study is to analyze and predict the power generation of different PV technologies under arid desert climate conditions on an hourly basis. Two areas have been considered as case studies: Adrar in Algeria and Alice Springs in Australia. A total of nine physical models and input parameter combinations from six different power plants have been used and tested for the suitability of the proposed models for predicting the power yield of PV power plants depending on solar irradiance and other meteorological variables. Then, an ensemble learning technique is applied to improve the performance capabilities of the best-fit input combinations. The results reveal that the global irradiance, ambient air temperature, and relative humidity combination are the most related to the PV power output of all technologies under all-sky conditions and provide effective and efficient performance with the proposed ensemble learning, with an estimated accuracy of over 99%.