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

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 anal...

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Bibliographic Details
Published in:Theoretical and applied climatology 2022-10, Vol.150 (1-2), p.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: Article
Language:English
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
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Summary: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%.
ISSN:0177-798X
1434-4483
DOI:10.1007/s00704-022-04166-6