scholarly journals CH<sub>4</sub> parameter estimation in CLM4.5bgc using surrogate global optimization

2015 ◽  
Vol 8 (10) ◽  
pp. 3285-3310 ◽  
Author(s):  
J. Müller ◽  
R. Paudel ◽  
C. A. Shoemaker ◽  
J. Woodbury ◽  
Y. Wang ◽  
...  
Keyword(s):  
Global Optimization ◽  
Parameter Estimation ◽  
Objective Function ◽  
Methane Emission ◽  
Optimization Algorithm ◽  
Model Parameters ◽  
Ch4 Emission ◽  
Community Land Model ◽  
Model Predictions ◽  
Pseudo Data

Abstract. Over the anthropocene methane has increased dramatically. Wetlands are one of the major sources of methane to the atmosphere, but the role of changes in wetland emissions is not well understood. The Community Land Model (CLM) of the Community Earth System Models contains a module to estimate methane emissions from natural wetlands and rice paddies. Our comparison of CH4 emission observations at 16 sites around the planet reveals, however, that there are large discrepancies between the CLM predictions and the observations. The goal of our study is to adjust the model parameters in order to minimize the root mean squared error (RMSE) between model predictions and observations. These parameters have been selected based on a sensitivity analysis. Because of the cost associated with running the CLM simulation (15 to 30 min on the Yellowstone Supercomputing Facility), only relatively few simulations can be allowed in order to find a near-optimal solution within an acceptable time. Our results indicate that the parameter estimation problem has multiple local minima. Hence, we use a computationally efficient global optimization algorithm that uses a radial basis function (RBF) surrogate model to approximate the objective function. We use the information from the RBF to select parameter values that are most promising with respect to improving the objective function value. We show with pseudo data that our optimization algorithm is able to make excellent progress with respect to decreasing the RMSE. Using the true CH4 emission observations for optimizing the parameters, we are able to significantly reduce the overall RMSE between observations and model predictions by about 50 %. The methane emission predictions of the CLM using the optimized parameters agree better with the observed methane emission data in northern and tropical latitudes. With the optimized parameters, the methane emission predictions are higher in northern latitudes than when the default parameters are used. For the tropics, the optimized parameters lead to lower emission predictions than the default parameters.

scholarly journals CH<sub>4</sub> parameter estimation in CLM4.5bgc using surrogate global optimization

2015 ◽  
Vol 8 (1) ◽  
pp. 141-207 ◽  
Author(s):  
J. Müller ◽  
R. Paudel ◽  
C. A. Shoemaker ◽  
J. Woodbury ◽  
Y. Wang ◽  
...  
Keyword(s):  
Global Optimization ◽  
Parameter Estimation ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Efficient Global Optimization ◽  
Model Parameters ◽  
Ch4 Emission ◽  
Community Land Model ◽  
Model Predictions ◽  
Pseudo Data

Abstract. Over the anthropocene methane has increased dramatically. Wetlands are one of the major sources of methane to the atmosphere, but the role of changes in wetland emissions is not well understood. The Community Land Model (CLM) of the Community Earth System Models contains a module to estimate methane emissions from natural wetlands and rice paddies. Our comparison of CH4 emission observations at 16 sites around the planet reveals, however, that there are large discrepancies between the CLM predictions and the observations. The goal of our study is to adjust the model parameters in order to minimize the root mean squared error (RMSE) between model predictions and observations. These parameters have been selected based on a sensitivity analysis. Because of the cost associated with running the CLM simulation (15 to 30 min on the Yellowstone Supercomputing Facility), only relatively few simulations can be allowed in order to find a near optimal solution within an acceptable time. Our results indicate that the parameter estimation problem has multiple local minima. Hence, we use a computationally efficient global optimization algorithm that uses a radial basis function (RBF) surrogate model to approximate the objective function. We use the information from the RBF to select parameter values that are most promising with respect to improving the objective function value. We show with pseudo data that our optimization algorithm is able to make excellent progress with respect to decreasing the RMSE. Using the true CH4 emission observations for optimizing the parameters, we are able to significantly reduce the overall RMSE between observations and model predictions by about 50%. The CLM predictions with the optimized parameters agree for northern and tropical latitudes more with the observed data than when using the default parameters and the emission predictions are higher than with default settings in northern latitudes and lower than default settings in the tropics.

scholarly journals Determination of synchronous generator nonlinear model parameters based on power rejection tests using a gradient optimization algorithm

2017 ◽  
Vol 65 (4) ◽  
pp. 479-488 ◽  
Author(s):  
A. Boboń ◽  
A. Nocoń ◽  
S. Paszek ◽  
P. Pruski
Keyword(s):  
Parameter Estimation ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Least Squares Method ◽  
Synchronous Generator ◽  
Operating Conditions ◽  
Model Parameters ◽  
State Variables ◽  
Starting Point ◽  
Gradient Optimization

AbstractThe paper presents a method for determining electromagnetic parameters of different synchronous generator models based on dynamic waveforms measured at power rejection. Such a test can be performed safely under normal operating conditions of a generator working in a power plant. A generator model was investigated, expressed by reactances and time constants of steady, transient, and subtransient state in the d and q axes, as well as the circuit models (type (3,3) and (2,2)) expressed by resistances and inductances of stator, excitation, and equivalent rotor damping circuits windings. All these models approximately take into account the influence of magnetic core saturation. The least squares method was used for parameter estimation. There was minimized the objective function defined as the mean square error between the measured waveforms and the waveforms calculated based on the mathematical models. A method of determining the initial values of those state variables which also depend on the searched parameters is presented. To minimize the objective function, a gradient optimization algorithm finding local minima for a selected starting point was used. To get closer to the global minimum, calculations were repeated many times, taking into account the inequality constraints for the searched parameters. The paper presents the parameter estimation results and a comparison of the waveforms measured and calculated based on the final parameters for 200 MW and 50 MW turbogenerators.

scholarly journals A New Global Optimization Algorithm for a Class of Linear Fractional Programming

Mathematics ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 867 ◽  
Author(s):  
X. Liu ◽  
Y.L. Gao ◽  
B. Zhang ◽  
F.P. Tian
Keyword(s):  
Global Optimization ◽  
Lower Bound ◽  
Objective Function ◽  
Programming Problem ◽  
Optimization Algorithm ◽  
Fractional Programming ◽  
Original Problem ◽  
Global Optimization Algorithm ◽  
Linear Fractional Programming ◽  
Advantages And Disadvantages

In this paper, we propose a new global optimization algorithm, which can better solve a class of linear fractional programming problems on a large scale. First, the original problem is equivalent to a nonlinear programming problem: It introduces p auxiliary variables. At the same time, p new nonlinear equality constraints are added to the original problem. By classifying the coefficient symbols of all linear functions in the objective function of the original problem, four sets are obtained, which are I i + , I i − , J i + and J i − . Combined with the multiplication rule of real number operation, the objective function and constraint conditions of the equivalent problem are linearized into a lower bound linear relaxation programming problem. Our lower bound determination method only needs e i T x + f i ≠ 0 , and there is no need to convert molecules to non-negative forms in advance for some special problems. A output-space branch and bound algorithm based on solving the linear programming problem is proposed and the convergence of the algorithm is proved. Finally, in order to illustrate the feasibility and effectiveness of the algorithm, we have done a series of numerical experiments, and show the advantages and disadvantages of our algorithm by the numerical results.

A New Global Optimization Algorithm and its Application

2008 ◽  
Vol 33-37 ◽  
pp. 1407-1412
Author(s):  
Ying Hui Lu ◽  
Shui Lin Wang ◽  
Hao Jiang ◽  
Xiu Run Ge
Keyword(s):  
Global Optimization ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Inverse Analysis ◽  
Optimization Problem ◽  
Practical Application ◽  
Global Optimization Algorithm ◽  
Material Parameters ◽  
Non Linear ◽  
Variable Space

In geotechnical engineering, based on the theory of inverse analysis of displacement, the problem for identification of material parameters can be transformed into an optimization problem. Commonly, because of the non-linear relationship between the identified parameters and the displacement, the objective function bears the multimodal characteristic in the variable space. So to solve better the multimodal characteristic in the non-linear inverse analysis, a new global optimization algorithm, which integrates the dynamic descent algorithm and the modified BFGS (Brogden-Fletcher-Goldfrab-Shanno) algorithm, is proposed. Five typical multimodal functions in the variable space are tested to prove that the new proposed algorithm can quickly converge to the best point with few function evaluations. In the practical application, the new algorithm is employed to identify the Young’s modulus of four different materials. The results of the identification further show that the new proposed algorithm is a very highly efficient and robust one.

scholarly journals Multi-mission satellite remote sensing data for improving land hydrological models via data assimilation

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
M. Khaki ◽  
H.-J. Hendricks Franssen ◽  
S. C. Han
Keyword(s):  
Remote Sensing ◽  
Parameter Estimation ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Satellite Remote Sensing ◽  
Model Parameters ◽  
Parameter Uncertainties ◽  
Area Index ◽  
Mission Satellite ◽  
Model Predictions

Abstract Satellite remote sensing offers valuable tools to study Earth and hydrological processes and improve land surface models. This is essential to improve the quality of model predictions, which are affected by various factors such as erroneous input data, the uncertainty of model forcings, and parameter uncertainties. Abundant datasets from multi-mission satellite remote sensing during recent years have provided an opportunity to improve not only the model estimates but also model parameters through a parameter estimation process. This study utilises multiple datasets from satellite remote sensing including soil moisture from Soil Moisture and Ocean Salinity Mission and Advanced Microwave Scanning Radiometer Earth Observing System, terrestrial water storage from the Gravity Recovery And Climate Experiment, and leaf area index from Advanced Very-High-Resolution Radiometer to estimate model parameters. This is done using the recently proposed assimilation method, unsupervised weak constrained ensemble Kalman filter (UWCEnKF). UWCEnKF applies a dual scheme to separately update the state and parameters using two interactive EnKF filters followed by a water balance constraint enforcement. The performance of multivariate data assimilation is evaluated against various independent data over different time periods over two different basins including the Murray–Darling and Mississippi basins. Results indicate that simultaneous assimilation of multiple satellite products combined with parameter estimation strongly improves model predictions compared with single satellite products and/or state estimation alone. This improvement is achieved not only during the parameter estimation period ($$\sim $$ ∼  32% groundwater RMSE reduction and soil moisture correlation increase from $$\sim $$ ∼  0.66 to $$\sim $$ ∼  0.85) but also during the forecast period ($$\sim $$ ∼  14% groundwater RMSE reduction and soil moisture correlation increase from $$\sim $$ ∼  0.69 to $$\sim $$ ∼  0.78) due to the effective impacts of the approach on both state and parameters.

Parameter estimation of single diode PV module based on Nelder-Mead optimization algorithm

2018 ◽  
Vol 15 (1) ◽  
pp. 70-81 ◽  
Author(s):  
Alivarani Mohapatra ◽  
Byamakesh Nayak ◽  
Kanungo Barada Mohanty
Keyword(s):  
Parameter Estimation ◽  
Optimization Algorithm ◽  
Environmental Conditions ◽  
Reference Model ◽  
Model Parameters ◽  
Unknown Parameters ◽  
Pv System ◽  
Content Type ◽  
Pv Module ◽  
Proposed Model

Purpose This paper aims to propose a simple, derivative-free novel method named as Nelder–Mead optimization algorithm to estimate the unknown parameters of the photovoltaic (PV) module considering the environmental conditions. Design/methodology/approach At a particular temperature and irradiation, experimental current-voltage (I-V) and power-voltage (P-V) characteristics are drawn and considered as a reference model. The PV system model with unknown model parameters is considered as the adaptive model whose unknown model parameters are to be adapted so that the simulated characteristics closely matches with the experimental characteristics. A single diode (Rsh) model with five unknown model parameters is considered here for the parameter estimation. Findings The key advantages of this method are that parameters are estimated considering environmental conditions. Experimental characteristics are considered for parameter estimation which gives accurate results. Parameters are estimated considering both I-V and P-V curves as most of the applications demand extraction of the actual power from the PV module. Originality/value The proposed model is compared with other three well-known models available in the literature considering various statistical errors. The results show the superiority of the proposed model with a minimum error for both I-V and P-V characteristics.

Differential simulated annealing: a robust and efficient global optimization algorithm for parameter estimation of biological networks

2014 ◽  
Vol 10 (6) ◽  
pp. 1385-1392 ◽  
Author(s):  
Ziwei Dai ◽  
Luhua Lai
Keyword(s):  
Global Optimization ◽  
Parameter Estimation ◽  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Biological Networks ◽  
Efficient Global Optimization ◽  
Global Optimization Algorithm ◽  
Large Networks

DSA outperformed five other algorithms in parameter estimation of 95 biological networks and showed significant advantage in large networks.

scholarly journals Mathematical model parameter estimation of a generating unit operating in the Polish National Power System

2016 ◽  
Vol 64 (2) ◽  
pp. 409-416
Author(s):  
Ł. Majka ◽  
S. Paszek
Keyword(s):  
Mathematical Model ◽  
Parameter Estimation ◽  
Power System ◽  
Objective Function ◽  
Model Parameters ◽  
Gradient Algorithms ◽  
Hybrid Optimization Algorithm ◽  
National Power ◽  
The Mathematical Model ◽  
Paper Method

Abstract In the paper, a method and results of parameter estimation of the mathematical model of a generating unit operating in the Polish National Power System are presented. Computations of the parameters were carried out based on measurement and simulation of dynamic waveforms of selected quantities of the generating unit. The problem of parameter identification was brought to minimization of the objective function determined by the vector of deviations between the approximated and approximating waveforms computed on the basis of the models expressed by the searched parameters. A hybrid optimization algorithm, being a serial combination of genetic and gradient algorithms, was used for minimization of the objective function. A methodology for filtering the recorded measurement signals is proposed in the paper. Method and results calculation of the sensitivity to changes of the model parameters of selected dynamic waveforms are also presented.

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