Narasimalu Srikanth

Bio: Narasimalu Srikanth is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Turbine & Wire bonding. The author has an hindex of 33, co-authored 168 publications receiving 3761 citations. Previous affiliations of Narasimalu Srikanth include National University of Singapore & ASM International.

Ensemble methods for wind and solar power forecasting—A state-of-the-art review

Abstract: This paper reviews state-of-the-art on wind speed/power forecasting and solar irradiance forecasting with ensemble methods. The ensemble forecasting methods are grouped into two main categories: competitive ensemble forecasting and cooperative ensemble forecasting. The competitive ensemble forecasting is further categorized based on data diversity and parameter diversity. The cooperative ensemble forecasting is divided according to pre-processing and post-processing. Typical articles are discussed according to each category and their characteristics are highlighted. We also conduct comparisons based on reported results and comparisons based on simulations conducted by us. Suggestions for future research include ensemble of different paradigms and inter-category ensemble methods among others.

A Comparative Study of Empirical Mode Decomposition-Based Short-Term Wind Speed Forecasting Methods

Abstract: Wind speed forecasting is challenging due to its intermittent nature. The wind speed time series (TS) has nonlinear and nonstationary characteristics and not normally distributed, which make it difficult to be predicted by statistical or computational intelligent methods. Empirical mode decomposition (EMD) and its improved versions are powerful tools to decompose a complex TS into a collection of simpler ones. The improved versions discussed in this paper include ensemble EMD (EEMD), complementary EEMD (CEEMD), and complete EEMD with adaptive noise (CEEMDAN). The EMD and its improved versions are hybridized with two computational intelligence-based predictors: support vector regression (SVR) and artificial neural network (ANN). The EMD-based hybrid forecasting methods are evaluated with 12 wind speed TS. The performances of the hybrid methods are compared and discussed. It shows that EMD and its improved versions enhance the performance of SVR significantly but marginally on ANN, and among the EMD-based hybrid methods, the proposed CEEMDAN-SVR is the best method. Possible future works are also recommended for wind speed forecasting.

Dependence of elastic and optical properties on surface terminated groups in two-dimensional MXene monolayers: a first-principles study

Abstract: Density functional theory is used to investigate the elastic and optical properties as well as the crystal and electronic structures of two-dimensional Ti2CT2 and Ti3C2T2 (T = F, O, and OH) MXene monolayers. It is found that the elastic stiffness, optical response, crystal structure and the electronic structure show strong dependence on the surface terminated groups often formed with MXene during the etching process. The elastic stiffness maintains only with the surface termination of O atoms, but a large degradation is present in the surface terminations of F and OH atoms. The low adsorption and reflectivity in the range from infrared to ultraviolet rays account for the high transmittance of Ti3C2T2 that has been experimentally observed, and it is predicted that Ti2CT2 will have higher optical transmittance in this range. The calculations also demonstrate the presence of the optical bandgap in Ti2CO2, which renders its potential applications in optical and electronic devices.

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Artificial neural networks

Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Guidelines and trends for next-generation rechargeable lithium and lithium-ion batteries.

Abstract: Commercial lithium-ion (Li-ion) batteries suffer from low energy density and do not meet the growing demands of the energy storage market. Therefore, building next-generation rechargeable Li and Li-ion batteries with higher energy densities, better safety characteristics, lower cost and longer cycle life is of outmost importance. To achieve smaller and lighter next-generation rechargeable Li and Li-ion batteries that can outperform commercial Li-ion batteries, several new energy storage chemistries are being extensively studied. In this review, we summarize the current trends and provide guidelines towards achieving this goal, by addressing batteries using high-voltage cathodes, metal fluoride electrodes, chalcogen electrodes, Li metal anodes, high-capacity anodes as well as useful electrolyte solutions. We discuss the choice of active materials, practically achievable energy densities and challenges faced by the respective battery systems. Furthermore, strategies to overcome remaining challenges for achieving energy characteristics are addressed in the hope of providing a useful and balanced assessment of current status and perspectives of rechargeable Li and Li-ion batteries.