Component Model Selection Research Articles

Optimized selection of component models for photovoltaic and energy storage system simulations

Photovoltaic (PV) systems have become an integral and widespread part of renewable energy generation. In combination with energy storage, they offer a variety of advantages such as increased self-sufficiency or improved grid stability. However, geographic location, subsidies, energy tariffs, size of PV panels and battery, and other factors significantly influence the overall performance and profitability of the system. Solving this complex task of system design and component dimensioning is not easy. The diversity and multitude of parameters therefore usually requires the help of, more or less complex, computer simulations. This led and still leads to a high publication activity on techno-economic and other optimizations for such systems. However, the operating strategies and component models are often oversimplified, leading to results with significant errors. The goal of this paper is to provide in-depth insight into component modeling and parametrization for PV module, battery energy storage, and inverter, as well as giving suggestions on appropriate control strategies. The focus lies on the description of suitable simple-but-accurate models and their parametrization. In order to avoid the effort of having to conduct measurements for component characterization, an approach relying on values presented in product data sheets is considered. Furthermore, the effects of model simplifications, as they are found in many publications, are shown. Finally, the importance of an uncertainty analysis for the entire simulation is discussed in order to gain insight into the accuracy of the calculated simulation results and their dependency on model- and input-parameters.

From digital to universal manufacturing

The transformation of the manufacturing industry over the last two decades has been largely inspired by data. Digitisation has made its mark in different areas of manufacturing, from digital materials and processes to data science in decision-making. The digital manufacturing is evolving towards universal manufacturing that is highlighted in this paper. Different manufacturing initiatives are benchmarked and their relationship to universal manufacturing is demonstrated. The representation of enterprises in the universal manufacturing cloud is discussed. Product- and process-based specifications of digital enterprises are defined. Using these specifications, the enterprise configuration algorithm proposed in the paper is applied for the selection of component models. Two different representations, a node–node and an input–output matrix, of the digital component models are considered. The extended topological sorting algorithm is applied to construct an integrated digital model.

Design and Research of CAS‐CIG for Earth System Models

AbstractThe Chinese Academy of Sciences Coupling Interface Generator (CAS‐CIG) is designed to address the complexities of the development and coupling of different component models in Earth System Models based on the Coupler 7 of the Community Earth System Model (CESM). Its application in the Chinese Academy of Sciences Earth System Model (CAS‐ESM) is described. The CAS‐CIG automatically generates the coupler code through a simple configuration file when a component model is accessed, enabling different component models to be easily ported to CPL7 to create simulation cases. Combined with the automatic generation of compile scripts, the precompilation and run directories are directly formed. The component model integration, model selection, experimental setup, and platform migration can be all accomplished in the CAS‐CIG. Verification of the CAS‐CIG is presented to show that the automatically generated codes can identically reproduce the simulation results of CAS‐ESM. CAS‐CIG presents a software tool for modeling centers to investigate the impact of component model selections on simulations of climate and weather.

An Algorithm to Identify Target-Selective Ligands - A Case Study of 5-HT7/5-HT1A Receptor Selectivity.

A computational procedure to search for selective ligands for structurally related protein targets was developed and verified for serotonergic 5-HT7/5-HT1A receptor ligands. Starting from a set of compounds with annotated activity at both targets (grouped into four classes according to their activity: selective toward each target, not-selective and not-selective but active) and with an additional set of decoys (prepared using DUD methodology), the SVM (Support Vector Machines) models were constructed using a selective subset as positive examples and four remaining classes as negative training examples. Based on these four component models, the consensus classifier was then constructed using a data fusion approach. The combination of two approaches of data representation (molecular fingerprints vs. structural interaction fingerprints), different training set sizes and selection of the best SVM component models for consensus model generation, were evaluated to determine the optimal settings for the developed algorithm. The results showed that consensus models with molecular fingerprints, a larger training set and the selection of component models based on MCC maximization provided the best predictive performance.

A Comparison of Some Methods for the Selection of a Common Eigenvector Model for the Covariance Matrices of Two Groups

Two new nonparametric common principal component model selection procedures based on bootstrap distributions of the vector correlations of all combinations of the eigenvectors from two groups are proposed. The performance of these methods is compared in a simulation study to the two parametric methods previously suggested by Flury in 1988, as well as modified versions of two nonparametric methods proposed by Klingenberg in 1996 and then by Klingenberg and McIntyre in 1998. The proposed bootstrap vector correlation distribution (BVD) method is shown to outperform all of the existing methods in most of the simulated situations considered.

Framework for component model selection

Advances in component-oriented software development research have led, among others, to the birth of a large number of component models, which has caused difficulties to the software developers in selecting the suitable component models to be used. To overcome the difficulties, a framework for component model selection is proposed in this study. The framework allows software developer to specify the criteria of the component model that they would like to use and recommends the component model(s) that fits most of the criteria. An application of the framework shows that it has the potential to be used by the software developers in determining the suitable component models.

Simulation of fluid power circuits using artificial network models: Part 1: Selection of component models

Consideration is given to the transient analysis of fluid power circuits based solely upon a variety of artificial networks rather than the conventional mathematical modelling approach. Networks, trained using data from practical tests, are developed for an axial piston pump and filter, a proportional pressure relief valve, a lumped volume and a subsystem containing a proportional flow control valve driving an axial piston motor with its integral load assembly. A variety of networks are considered ranging from the linear-regression type, BARMAX, the self-organizing group method of data handling (GMDH) using nonlinear regression theory to multilayer perceptron (MLP) networks by the back propagation algorithm. Part 2 (1) then shows how the network models may be linked with the aid of additional steady state network models to predict the behaviour of a motor speed control system.