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Likelihood Based Inference In Cointegrated Vector Autoregressive Models

Cell modelling and model parameters estimation techniques for photovoltaic simulator application: A review

Historically, time series forecasts of economic variables have used only a handful of predictor variables, while forecasts based on a large number of predictors have been the province of judgmental forecasts and large structural econometric models. The past decade, however, has seen considerable progress in the development of time series forecasting methods that exploit many predictors, and this chapter surveys these methods. The first group of methods considered is forecast combination (forecast pooling), in which a single forecast is produced from a panel of many forecasts. The second group of methods is based on dynamic factor models, in which the comovements among a large number of economic variables are treated as arising from a small number of unobserved sources, or factors. In a dynamic factor model, estimates of the factors (which become increasingly precise as the number of series increases) can be used to forecast individual economic variables. The third group of methods is Bayesian model averaging, in which the forecasts from very many models, which differ in their constituent variables, are averaged based on the posterior probability assigned to each model. The chapter also discusses empirical Bayes methods, in which the hyperparameters of the priors are estimated. An empirical illustration applies these different methods to the problem of forecasting the growth rate of the U.S. index of industrial production with 130 predictor variables.

Forecasting with Many Predictors

The contribution of solar photovoltaics (PV׳s) in generation of electric power is continually increasing. PV cells are commonly modelled as circuits. Finding appropriate circuit model parameters of PV cells is crucial for performance evaluation, control, efficiency computations and maximum power point tracking of solar PV systems. The problem of finding circuit model parameters of solar PV cells is referred to as “PV cell model parameter estimation problem,” and is highly attracted by researchers. In this paper, the existing research works on PV cell model parameter estimation problem are classified into three categories and the research works of those categories are reviewed. Based on the conducted review, some recommendations for future research are provided.

Parameter estimation of solar photovoltaic (PV) cells: A review

An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models

A three diode model for industrial solar cells and estimation of solar cell parameters using PSO algorithm

A Simulation model for simulation of a single solar cell and two solar cells in series has been developed using Simelectronics (Matlab/Simulink) environment and is presented here in this paper.Â  A solar cell block is available in simelectronics, which was used with many other blocks to plot I-V and P-V characteristics under variations of parameters considering one parameter variation at a time. The effect of variation of parameters such as series resistance, R s , shunt resistance R sh , diode parameters: diode saturation current, I s and ideality factor, N, could be seen on the characteristics of a single solar cell. Effect of two environmental parameters of temperature and irradiance variations could also be observed from simulated characteristics.Â  Matlab coding has been done to find the maximum power output, P m , and voltage at maximum power output, V m , of a single solar cell and two solar cells (in series) under different values of parameters.Â  The P m and V m values are tabulated here in this paper for variation of one parameter at a time, considering the diode parameters: I s and N, resistances: series and shunt, temperature and irradiance.

https://www.ijrer.com/index.php/ijrer/article/download/416/pdf

MATLAB/SIMELECTRONICS Models Based Study of Solar Cells

The present study demonstrates the application of artificial neural networks (ANNs) in predicting the weekly spring discharge. The study was based on the weekly spring discharge from a spring located near Ranichauri in Tehri Garhwal district of Uttarakhand, India. Five models were developed for predicting the spring discharge based on a weekly interval using rainfall, evaporation, temperature with a specified lag time. All models were developed both with one and two hidden layers. Each model was developed with many trials by selecting different network architectures and different number of hidden neurons; finally a best predicting model presented against each developed model. The models were trained with three different algorithms, that is, quick-propagation algorithm, batch backpropagation algorithm, and Levenberg-Marquardt algorithm using weekly data from 1999 to 2005. A best model for the simulation was selected from the three presented algorithms using the statistical criteria such as correlation coefficient (R), determination coefficient, orNash Sutcliff's efficiency (DC). Finally, optimized number of neurons were considered for the best model. Training and testing results revealed that the models were predicting the weekly spring discharge satisfactorily. Based on these criteria, ANN-based model results in better agreement for the computation of spring discharge. LMR models were also developed in the study, and they also gave good results, but, when compared with the ANN methodology, ANN resulted in better optimized values.

https://downloads.hindawi.com/archive/2011/686258.pdf

Development of artificial neural-network-based models for the simulation of spring discharge

Soft Computing tools are becoming very popular in solving hydrological problems. These tools have immense strength to deal with such complex problems. Water Table elevation estimation is an important aspect to understand the mechanism of ground water resources. The present study aims at the application of Artificial Neural Networks (ANN) & Fuzzy logic for simulation of water table elevation. This paper also investigates the best model to forecast water table elevation. Ten ANN models are developed in this study. These developed models are trained, tested and validated on the available data of Budaun District. Comparing observed data and the estimated data through developed ANN models and Fuzzy models, it has been observed that the developed Fuzzy models predict better results for four models and for model-5 ANN bore better results.

Dinesh C. S. Bisht

Papers

A three diode model for industrial solar cells and estimation of solar cell parameters using PSO algorithm

MATLAB/SIMELECTRONICS Models Based Study of Solar Cells

Development of artificial neural-network-based models for the simulation of spring discharge

Prediction of Water Table Elevation Fluctuation through Fuzzy Logic & Artificial Neural Networks

Trisectional fuzzy trapezoidal approach to optimize interval data based transportation problem