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A new prediction model based on cascade NN for wind power prediction
, Article Computational Economics ; March , 2018 , Pages 1-25 ; 09277099 (ISSN) ; Kiaian Mousavy, S. A ; Dashti, V ; Saeedi, M ; Yousefi, N ; Sharif University of Technology
Springer New York LLC
2018
Abstract
This paper presents a new prediction model based on empirical mode decomposition, feature selection and hybrid forecast engine. The whole structure of proposed model is based on nonstationarity and non-convex nature of wind power signal. The hybrid forecast engine consists of three main stages as; empirical mode decomposition, an intelligent algorithm and three stage neural network. All parameters of proposed neural network will be optimized by intelligent algorithm. Effectiveness of the proposed model is tested with real-world hourly data of wind farms in Canada, Spain and Texas. In order to demonstrate the validity of the proposed model, it is compared with several other wind speed and...
A new prediction model based on cascade NN for wind power prediction
, Article Computational Economics ; Volume 53, Issue 3 , 2019 , Pages 1219-1243 ; 09277099 (ISSN) ; Kiaian Mousavy, S. A ; Dashti, V ; Saeedi, M ; Yousefi, N ; Sharif University of Technology
Springer New York LLC
2019
Abstract
This paper presents a new prediction model based on empirical mode decomposition, feature selection and hybrid forecast engine. The whole structure of proposed model is based on nonstationarity and non-convex nature of wind power signal. The hybrid forecast engine consists of three main stages as; empirical mode decomposition, an intelligent algorithm and three stage neural network. All parameters of proposed neural network will be optimized by intelligent algorithm. Effectiveness of the proposed model is tested with real-world hourly data of wind farms in Canada, Spain and Texas. In order to demonstrate the validity of the proposed model, it is compared with several other wind speed and...
Structural Damage Detection by Using Signal-Based and Artificial Intelligence Methods; Case Study 'Moment-Resisting Frame Structure
, M.Sc. Thesis Sharif University of Technology ; Bakhshi, Amin (Supervisor) ; Bahar, Omid (Supervisor)
Abstract
Civil structures are on the verge of changing which leads energy dissipation capacity to decline. Structural Health Monitoring (SHM) as a process in order to implement a damage detection strategy and assess the condition of structure plays a key role in structural reliability. Earthquake is a recognized factor in variation of structures condition, inasmuch as inelastic behavior of a building subjected to design level earthquakes is plausible. In this study Hilbert Hunag Transformation (HHT) is superseded by Ensemble Empirical Mode decomposition (EEMD) and Hilbert Transform (HT) together. Albeit this method is closely resemble HHT, EEMD brings more appropriate Intrinsic Mode Functions (IMFs)....
Online nonlinear structural damage detection using hilbert Huang transform and artificial neural networks
, Article Scientia Iranica ; Volume 26, Issue 3A , 2019 , Pages 1266-1279 ; 10263098 (ISSN) ; Bakhshi, A ; Bahar, O ; Sharif University of Technology
Sharif University of Technology
2019
Abstract
In order to implement a damage detection strategy and assess the condition of a structure, Structural Health Monitoring (SHM) as a process plays a key role in structural reliability. This paper aims to present a methodology for online detection of damages that may occur during a strong ground excitation. In this regard, Empirical Mode Decomposition (EMD) is superseded by Ensemble Empirical Mode Decomposition (EEMD) in the Hilbert Huang Transformation (HHT). Although analogous with EMD, EEMD brings about more appropriate Intrinsic Mode Functions (IMFs). IMFs are employed to assess the first-mode frequency and mode shape. Afterwards, Artificial Neural Network (ANN) is applied to predict story...
Online nonlinear structural damage detection using hilbert huang transform and artificial neural networks
, Article Scientia Iranica ; Volume 26, Issue 3A , 2019 , Pages 1266-1279 ; 10263098 (ISSN) ; Bakhshi, A ; Bahar, O ; Sharif University of Technology
Sharif University of Technology
2019
Abstract
In order to implement a damage detection strategy and assess the condition of a structure, Structural Health Monitoring (SHM) as a process plays a key role in structural reliability. This paper aims to present a methodology for online detection of damages that may occur during a strong ground excitation. In this regard, Empirical Mode Decomposition (EMD) is superseded by Ensemble Empirical Mode Decomposition (EEMD) in the Hilbert Huang Transformation (HHT). Although analogous with EMD, EEMD brings about more appropriate Intrinsic Mode Functions (IMFs). IMFs are employed to assess the first-mode frequency and mode shape. Afterwards, Artificial Neural Network (ANN) is applied to predict story...