1,721,043 research outputs found
An Integrated Approach to shorten wind potential assessment
No full textThe growth of new wind projects continues to be hampered by the lack of wind resource data. Such data are needed to enable governments and/or private developers to identify potential areas suitable for development. The principal aim of the work is the formalization of an innovative approach that hastens a robust parameter estimation of statistical models on anemometric data. The developed methodologies have been theoretical and have concerned: -parameter estimation of wind speed stochastic model, based on short-run samples, able to integrate both the information contained in the wind atlases, and the expert opinion through a Practical Bayesian approach, after an opportune data filtering strategy; -the determination of new "plotting positions", crucial for the graphical estimation of the parameters in the engineering and environmental fields. Particular attention has been given to the application on the (asymptotic) distributions of extreme values, widely used in environmental modeling and in "return period." estimation. Such methodologies have also been implemented via numerical code developed in Mathematica® e/o Matlab®. Particularly, computational problems arisen from the proposed Bayesian estimation are faced through MCMC (Markov Chain Monte Carlo) technique, numerically implemented in R/WinBugs. The aforementioned methodologies are proposed as solution to real problems raised by renewable energies companies. The interaction with the latter was essential to elicitate the expert opinion on the wind sites and for allowing methodologies to be validated on real anemometric data, collected from southern Italian sites. It has been possible to observe (ex post) smaller estimation errors in comparison to those that would be derived from the application of the usual estimation methods presented in the literature
New plotting positions approoach to reliability estimates from small and censored samples
No full textA Bayesian approach to boost wind parameter estimation by fusing historical and sampling data
No full textIn the last decade wind energy has proven to be one of the most competitive and fastest growing sources of renewable energy. Currently the problem of evaluating the site-specific wind potential is showing the main difficulties since it is faced only using on-site anemometric long-term monitoring and wind atlases.
Usually information published on wind atlases traces a territorial map of the wind speeds in correspondence with different heights. This documents represent a good starting-point for a preliminary site analysis, but it fails to give reliable customized information to support the evaluation and, eventually, the activation of potential investments. Undoubtedly, the on-site direct anemometric monitoring gives more reliable information because it is directly taken from the site. However, it is costly and lengthy because wide temporal windows (of at least one year) are required to accurately characterize the site-specific wind potential. In order to obtain a both reliable and timely analysis, this paper proposes to integrate the above two sources of knowledge by a Bayesian approach, implemented via Markov chain Monte Carlo (MCMC). The proposed methodology, combining prior information (e.g. obtained from atlases and/or fluid-dynamic assessment) with sampling data, furnishes robust and timely posterior information.
Real sampling data, collected from a southern Italian site, are analysed in order to illustrate the main features of the proposed methodology and to test the adopted filtering strategy to face the high correlation which characterize the anemometric data.
The results of the example show that the proposed Bayes methodology fits the applicative needs very well. In particular the attained precision of the estimates carried out from one-month sample is comparable to the one of the ML estimates from the corresponding whole (one-year) sample
An Adaptive Multivariate Functional Control Chart
No full textNew data acquisition technologies allow one to gather amounts of data that are best represented as functional data. In this setting, profile monitoring assesses the stability over time of both univariate and multivariate functional quality characteristics. The detection power of profile monitoring methods could heavily depend on parameter selection criteria, which usually do not take into account any information from the out-of-control (OC) state. This work proposes a new framework, referred to as adaptive multivariate functional control chart (AMFCC), capable of adapting the monitoring of a multivariate functional quality characteristic to the unknown OC distribution by combining p-values of partial tests corresponding to Hotelling (Formula presented.) -type statistics calculated at different parameter combinations. Through an extensive Monte Carlo simulation study, the performance of AMFCC is compared with methods that have already appeared in the literature. Finally, a case study is presented in which the proposed framework is used to monitor a resistance spot welding process in the automotive industry. AMFCC is implemented in the (Formula presented.) package (Formula presented.), available on CRAN
Quality Analysis of 3D Reconstruction in Underwater Photogrammetry by Bootstrapping Design of Experiments
No full textOrthogonal LS-PLS approach to ship fuel-speed curves for supporting decisions based on operational data
No full textThe shipping industry relies on ship fuel-speed curves to describe the fuel consumption (and CO2 emissions levels) per hour as a function only of the vessel’s speed over ground, based on dedicated test data. However, they are affected by additional factors in real cases. In this article, a novel method is developed elaborating the orthogonal least-squares partial least-squares (LS-PLS) approach to enhance fuel-speed curves accuracy when information is available on additional factors from multi-sensor systems. Through real data examples, the approach is shown capable of detecting anomalies in CO2 emission levels and testing the effectiveness of ship energy efficiency initiatives
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