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Abstract: This paper studies the feasibility of utilizing photovoltaic systems in a standard residential apartment in Amman city in Jordan. Data on solar radiation, sunshine duration and the ambient temperature has been recorded in Amman city. An apartment in Amman was chosen as a case study to conduct energy and economic calculations. The electrical power needs and cost were calculated for the apartment. The component design and cost of PV system required to supply required energy was calculated and the payback period for the suggested stand-alone PV system in this paper was estimated in a constant inflation rate in electricity price similar to that of interest rate. The calculated payback period was high in a stand-alone system, to decrease payback period a grid-connected PV system is suggested. Considering an annual increase of 3% in electricity price, 15% of payback period was decreased in a stand-alone PV system and 21% in a grid-connected PV system. The output results of this study show that installation of PV system in a residential flat in Jordan may not be economically rewarding owing to the high cost of PV system compared to the cost of grid electricity. A feed-in tariff law of solar electricity may help to reduce PV system cost like the case of Germany. Additional conclusions are PV systems may be economically rewarding in Jordan if applied in locations far from electrical grid or in remote large scale PV power installations to overcome economical limitations of PV technology. [Copyright &y& Elsevier]

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%. [ABSTRACT FROM AUTHOR]