A case study in the tropical region to evaluate univariate imputation methods for solar irradiance data with different weather types

2022 ◽  
Vol 50 ◽  
pp. 101764
Noor Bariah Mohamad ◽  
An-Chow Lai ◽  
Boon-Han Lim
2019 ◽  
Vol 11 (2) ◽  
pp. 177-205 ◽  
Fadhil Y. Al-Aboosi

AbstractThe precise estimation of solar radiation data is substantial in the long-term evaluation for the techno-economic performance of solar energy conversion systems (e.g., concentrated solar thermal collectors and photovoltaic plants) for each site around the world, particularly, direct normal irradiance which is utilized commonly in designing solar concentrated collectors. However, the lack of direct normal irradiance data comparing to global and diffuse horizontal irradiance data and the high cost of measurement equipment represent significant challenges for exploiting and managing solar energy. Consequently, this study was performed to develop two hierarchical methodologies by using various models, empirical correlations and regression equations to estimate hourly solar irradiance data for various worldwide locations (using new correlation coefficients) and different sky conditions (using cloud cover range). Additionally, the preliminary assessment for the potential of solar energy in the selected region was carried out by developing a comprehensive analysis for the solar irradiance data and the clearness index to make a proper decision for the capability of utilizing solar energy technologies. A case study for the San Antonio region in Texas was selected to demonstrate the accuracy of the proposed methodologies for estimating hourly direct normal irradiance and monthly average hourly direct normal irradiance data at this region. The estimated data show a good accuracy comparing with measured solar data by using locally adjusted coefficients and different statistical indicators. Furthermore, the obtained results show that the selected region is unequivocally amenable to harnessing solar energy as the prime source of energy by utilizing concentrating and non-concentrating solar energy systems.

Solar Energy ◽  
2021 ◽  
Vol 216 ◽  
pp. 508-517
Grant Buster ◽  
Michael Rossol ◽  
Galen Maclaurin ◽  
Yu Xie ◽  
Manajit Sengupta

2015 ◽  
Vol 719-720 ◽  
pp. 596-599
Xin Wen Duan ◽  
Yue Zhang

The application of virtual instrument technology to design solar irradiance acquisition system, an ideal combination of software and hardware, is aimed at collecting, storing and analyzing data of external temperature and solar irradiance.The data proves helpful in assessing whether the solar energy resource deserves to be developded economically.The system is reliable and has been verified by simulation software proteus.

2019 ◽  
Vol 141 (6) ◽  
Edith Osorio de la Rosa ◽  
Guillermo Becerra Nuñez ◽  
Alfredo Omar Palafox Roca ◽  
René Ledesma-Alonso

This paper presents a methodology to estimate solar irradiance using an empiric-stochastic approach, which is based on the computation of normalization parameters from the solar irradiance data. For this study, the solar irradiance data were collected in a weather station during a year. Posttreatment included a trimmed moving average to smooth the data, the performance of a fitting procedure using a simple model to recover normalization parameters, and the estimation of a probability density, which evolves along the daytime, by means of a kernel density estimation method. The normalization parameters correspond to characteristic physical variables that allow us to decouple the short- and long-term behaviors of solar irradiance and to describe their average trends with simple equations. The normalization parameters and the probability densities allowed us to build an empiric-stochastic methodology that generates an estimate of the solar irradiance. Finally, in order to validate our method, we had run simulations of solar irradiance and afterward computed the theoretical generation of solar power, which in turn had been compared with the experimental data retrieved from a commercial photovoltaic system. Since the simulation results show a good agreement with the experimental data, this simple methodology can generate the synthetic data of solar power production and may help to design and test a photovoltaic system before installation.

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