Efficient least angle regression for identification of linear-in-the-parameters models

Probabilistic screening and behavior of solar cells under Gaussian parametric uncertainty using polynomial chaos representation model

The paper presents a hierarchical polynomial chaos expansion-based probabilistic approach to analyze the single diode solar cell model under Gaussian parametric uncertainty. It is important to analyze single diode solar cell model response under random events or factors due to uncertainty propagation. The optimal values of five electrical parameters associated with the single diode model are estimated using six deterministic optimization techniques through the root-mean-square minimization approach. Values corresponding to the best objective function response are further utilized to describe the probabilistic design space of each random electrical parameter under uncertainty. Adequate samples of each parameter corresponding Gaussian uncertain distribution are generated using Latin hypercube sampling. Furthermore, a multistage probabilistic approach is adopted to evaluate the model response using low-cost polynomial chaos series expansion and perform global sensitivity analysis under specified Gaussian distribution. Coefficients of polynomial basis functions are calculated using least square and least angle regression techniques. Unlike the highly non-linear and complex single diode representation of solar cells, the polynomial chaos expansion model provides a low computational burden and reduced complexity. To ensure reproducibility, probabilistic output response computed using proposed polynomial chaos expansion model is compared with the true model response. Finally, a multidimensional sensitivity analysis is performed through Sobol decomposition of polynomial chaos series representation to quantify the contribution of each parameter to the variance of the probabilistic response. The validation and assessment result shows that the output probabilistic response of the solar cell under Gaussian parametric uncertainty correlates to a Rayleigh probability distribution function. Output response is characterized by a mean value of 0.0060 and 0.0760 for RTC France and Solarex MSX83 solar cells, respectively. The standard deviation of $$ \pm $$ ± 0.0034 and $$ \pm $$ ± 0.0052 was observed in the probabilistic response for RTC France and Solarex MSX83 solar cells, respectively.

Denitrification and the controlling factors in Yunnan Plateau Lakes (China): Exploring the role of enhanced internal nitrogen cycling by algal blooms

Denitrification plays an important role in nitrogen (N) removal in freshwater ecosystems. This internal process regulates the fluctuations of N concentration, especially for lakes with high nutrients concentrations and long residence time. Lakes in Yunnan plateau (southwestern China) provide typical cases, while studies in this region have been rare. Therefore, we studied denitrification of two lakes (Lake Dianchi in hypereutrophic state and Lake Erhai in mesotrophic) in this region. We used acetylene inhibition technique to quantify potential denitrification rate (PDR) of these lakes in April and August, 2015 and 2016. PDR of the sediments ranged 0-1.21 μmol/(N·m²·hr), and that of overlying water ranged 0-0.24 μmol/(N·L·hr). Then, we used Least Angle Regression to determine the controlling factors for denitrification. Nutrients controlled PDR from two aspects: providing essential nitrogen sources; and affecting the richness and metabolism of denitrifying bacteria. In April, both aspects limited PDR; while only nitrogen sources limited PDR in August, due to depleted nitrate and enhanced denitrifying bacteria activity. Ammonia was most significant to denitrification, indicating that nitrate from nitrification transported to the bottom of well-mixed lake provide major N source by denitrification. The high PDR and low nitrate concentrate in August were evidence of an enhanced internal N cycling by algal blooms.