a b s t r a c tThis paper presents a method to improve the accuracy of artificial neural network (ANN)ebased estimation of photovoltaic (PV) power output by introducing two more inputs, solar zenith angle and solar azimuth angle, in addition to the most widely used environmental information, plane-of-array irradiance and module temperature. Solar zenith angle and solar azimuth angle define the solar position in the sky; hence, the loss of modeling accuracy due to impacts of solar angle-of-incidence and solar spectrum is reduced or eliminated. The observed data from two sites where local climates are significantly different is used to train and test the proposed network. The good performance of the proposed network is verified by comparing with existing ANN model, algebraic model, and polynomial regression model which use environmental information only of plane-of-array irradiance and module temperature. Our results show that the proposed ANN model greatly improves the accuracy of estimation in the long term under various weather conditions. It is also demonstrated that the improvement in estimating outdoor PV power output by adding solar zenith angle and azimuth angle as inputs is useful for other data-driven methods like support vector machine regression and Gaussian process regression.

importance for the anticipation, mitigation, and containment of pandemics. Disease-spread simulation models are often used to understand the effects of changes in citizen behavior or government policies, or to study disease outbreak parameters and mitigation-strategy features. Here, we focus on how to improve future global pandemic containment with the help of advanced artifi cial intelligence and simulation methods.

Because of their low cost, photovoltaic (PV) cells made from upgraded metallurgical grade silicon (UMG-Si) are a promising alternative to conventional solar grade silicon-based PV cells. This study investigates the outdoor performance of a 1.26 kW grid-connected UMG-Si PV system over five years, reporting the energy yields and performance ratio and estimating the long-term performance degradation rate. To make this investigation more meaningful, the performance of a mono-Si PV system installed at the same place and studied during the same period of time is presented for reference. Furthermore, this study systematizes and rationalizes the necessity of a data selection and filtering process to improve the accuracy of degradation rate estimation. The impact of plane-of-array irradiation threshold for data filtering on performance ratio and degradation rate is also studied. The UMG-Si PV system's monthly performance ratio after data filtering ranged from 84% to 93% over the observation period. The annual degradation rate was 0.44% derived from time series of monthly performance ratio using the classical decomposition method. A comparison of performance ratio and degradation rate to conventional crystalline silicon-based PV systems suggests that performance of the UMG-Si PV system is comparable to that of conventional systems.

The optimum s o l a r pond design is site-dependent and application-dependent. Foremost of the design dec i s i o n s is the choice of a s a l t y (nonconvecting) pond o r a s a l t l e s s (convecting) pond. The decision varia b l e s are: l o c a l a v a i l a b i l i t y and c o s t of salt, type

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