5 research outputs found
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Rapid evaluation of micro-scale photovoltaic solar energy systems using empirical methods combined with deep learning neural networks to support systems' manufacturers
Solar energy is becoming one of the most attractive renewable sources. In many cases, due to a wide range of financial or installation limitations, off-grid small scale micro power panels are favoured as modular systems to power lighting in gardens or to be integrated together to power small devices such as mobile phone chargers and distributed smart city facilities and services. Manufacturers and systems' integrators have a wide range of options of micro-scale photo voltaic panels to choose from. This makes the selection of the right panel a challenging task and risky investment. To address this and to help manufacturers, this paper suggests and evaluates a novel approach based on integrating empirical lab-testing with short-term real data and neural networks to assess the performance of micro-scale photovoltaic panels and their suitability for a specific application in specific environment. The paper outlines the combination of lab testing power output under seasonal and hourly conditions during the year combined with environmental and operating conditions such as temperature, dust accumulation and tilt angle performance. Based on the lab results, a short in-situ experimental work is implemented and the performance over the year in the selected location in Kuwait is evaluated using deep learning neural networks. The findings of this approach are compared with simulation and long-term real data. The results show a maximum error of 23% of the neural network output when compared with the actual data, and a correlation values with previous work within 87.3% and 91.9% which indicate that the proposed approach could provide an experimental rapid and accurate assessment of the expected power output. Hence, supporting the rapid decision-making process for manufacturers and reducing investment risks
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Rapid evaluation of the design and manufacture of cooling systems of photovoltaic solar panels
A new methodology is presented in this paper to encourage the growth of renewable energy technologies in hot and arid countries. PV solar panels are characterized by a decrease in efficiency with the increase in temperatures. This means in hot sunny countries, the actual output will decrease, affecting the power output despite the high availability of sun irradiation. In order to address this issue, a new methodology has been developed and presented in this paper to support system's designers and manufacturers; which allows rapid testing and assessment of the design in consistent way within a short period of time. The approach, named Rapid Evaluation of Solar panels Cooling (RESC), is novel as it combines rapid laboratory testing, with in-situ experimental data to evaluate the cooling technologies that are integrated into solar panels. Modular and scalable designs of passive (chimney effect) and active (fan) cooling methods were tested. The results show that the suggested approach is successful in comparing between the cooling technologies to assess their performance and the payback period within a short period of time. Carbon savings are also calculated for the suggested cooling technologies. The results show that the best energy performance was found to be for the fan-cooled system with overall 12.3% improvement in annual energy output. However, when compared to the payback period on financial investment, the passive cooling is found to more appealing. The key advantage of cooling technologies is found to be in producing an additional significant level of power during summer days when the surface temperature of the panel is at 70 °C or above. Hence, in such conditions, the cooling process could result in an increase in power output of about 53.15% relative to the uncooled standard panels. List of symbols P m Power at maximum level of solar cell or panel I m Current at maximum power point V m Voltage at maximum power point F F Fill factor I SC Short circuit current V OC Open circuit voltage β re f Temperature coefficient T o The elevated temperature at which the PV efficiency is zer