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المؤلفTouati F.
المؤلفChowdhury N.A.
المؤلفBenhmed K.
المؤلفSan Pedro Gonzales A.J.R.
المؤلفAl-Hitmi M.A.
المؤلفBenammar M.
المؤلفGastli A.
المؤلفBen-Brahim L.
تاريخ الإتاحة2022-05-22T11:03:07Z
تاريخ النشر2017
اسم المنشورRenewable Energy
المصدرScopus
المعرّفhttp://dx.doi.org/10.1016/j.renene.2017.06.078
معرّف المصادر الموحدhttp://hdl.handle.net/10576/31440
الملخص?Solar photovoltaic (PV) energy in GCC?- the term seems convincing to many solar PV industries due to high solar exposure in GCC region. However, long-term effects such as dust accumulation and seasonal variation are major drawbacks for solar PV energy. This research aims to investigate PV performance for two years in the harsh environment of Qatar. For data collection, a wireless system has been developed to record critical parameters such as solar irradiance, relative humidity, ambient temperature, PV module temperature, dust, wind speed, and output PV power. Results show that due to panel dusting for eight months, the PV output power decreased by 50%. Also, owing to lower ambient temperatures, clearer sky and cleaner panels due to occasional rainfall, the PV panels show higher output power in Winter than in Summer season. Besides, within one-month, a cloudy condition in Winter causes 20% drop in average output power. Therefore, a strategic plan is needed to build and manage efficiently a PV solar plant in harsh environments such as of Qatar. Energy management requires prediction of energy yield. To this end, using machine-learning, a mathematical model has been established which can predict the output power from PV panels under different environmental conditions.
اللغةen
الناشرElsevier Ltd
الموضوعDust
Education
Forecasting
Learning systems
Photovoltaic cells
Temperature
Average output power
Dust accumulation
Environmental conditions
Environmental parameter
Long term performance
Photovoltaic energy
Power predictions
PV module temperature
Solar power generation
data acquisition
dust
environmental factor
long-term change
machine learning
numerical model
performance assessment
photovoltaic system
prediction
renewable resource
seasonal variation
solar power
Bahrain
Kuwait [Middle East]
Oman
Qatar
Saudi Arabia
United Arab Emirates
العنوانLong-term performance analysis and power prediction of PV technology in the State of Qatar
النوعArticle
الصفحات952-965
رقم المجلد113
dc.accessType Abstract Only


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