scholarly journals LSSVR Model of G-L Mixed Noise-Characteristic with Its Applications

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 629 ◽  
Author(s):  
Shiguang Zhang ◽  
Ting Zhou ◽  
Lin Sun ◽  
Wei Wang ◽  
Baofang Chang
Keyword(s):  
Wind Speed ◽  
Regression Models ◽  
Historical Data ◽  
Least Square ◽  
Noise Model ◽  
Noise Distribution ◽  
Short Term ◽  
Mixed Noise ◽  
Noise Models ◽  
Better Than

Due to the complexity of wind speed, it has been reported that mixed-noise models, constituted by multiple noise distributions, perform better than single-noise models. However, most existing regression models suppose that the noise distribution is single. Therefore, we study the Least square S V R of the Gaussian–Laplacian mixed homoscedastic ( G L M − L S S V R ) and heteroscedastic noise ( G L M H − L S S V R ) for complicated or unknown noise distributions. The ALM technique is used to solve model G L M − L S S V R . G L M − L S S V R is used to predict short-term wind speed with historical data. The prediction results indicate that the presented model is superior to the single-noise model, and has fine performance.

Research on Particle Swarm Optimization Least Square Support Vector Machine for Short-Term Wind Speed Prediction

2014 ◽  
Vol 511-512 ◽  
pp. 927-930
Author(s):  
Shuai Zhang ◽  
Hai Rui Wang ◽  
Jin Huang ◽  
He Liu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Wind Speed ◽  
Particle Swarm ◽  
Least Square ◽  
Support Vector ◽  
Short Term ◽  
Swarm Optimization ◽  
Wind Speed Prediction ◽  
Speed Prediction

In the paper, the forecast problems of wind speed are considered. In order to enhance the redaction accuracy of the wind speed, this article is about a research on particle swarm optimization least square support vector machine for short-term wind speed prediction (PSO-LS-SVM). Firstly, the prediction models are built by using least square support vector machine based on particle swarm optimization, this model is used to predict the wind speed next 48 hours. In order to further improve the prediction accuracy, on this basis, introduction of the offset optimization method. Finally large amount of experiments and measurement data comparison compensation verify the effectiveness and feasibility of the research on particle swarm optimization least square support vector machine for short-term wind speed prediction, Thereby reducing the short-term wind speed prediction error, very broad application prospects.

scholarly journals A Compound Structure for Wind Speed Forecasting Using MKLSSVM with Feature Selection and Parameter Optimization

2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
Sizhou Sun ◽  
Jingqi Fu ◽  
Feng Zhu ◽  
Nan Xiong
Keyword(s):  
Feature Selection ◽  
Wind Speed ◽  
Search Algorithm ◽  
Least Square ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Wind Speed Prediction ◽  
Speed Prediction

The aims of this study contribute to a new hybrid model by combining ensemble empirical mode decomposition (EEMD) with multikernel function least square support vector machine (MKLSSVM) optimized by hybrid gravitation search algorithm (HGSA) for short-term wind speed prediction. In the forecasting process, EEMD is adopted to make the original wind speed data decomposed into intrinsic mode functions (IMFs) and one residual firstly. Then, partial autocorrelation function (PACF) is applied to identify the correlation between the corresponding decomposed components. Subsequently, the MKLSSVM using multikernel function of radial basis function (RBF) and polynomial (Poly) kernel function by weight coefficient is exploited as core forecasting engine to make the short-term wind speed prediction. To improve the regression performance, the binary-value GSA (BGSA) in HGSA is utilized as feature selection approach to remove the ineffective candidates and reconstruct the most relevant feature input-matrix for the forecasting engine, while real-value GSA (RGSA) makes the parameter combination optimization of MKLSSVM model. In the end, these respective decomposed subseries forecasting results are combined into the final forecasting values by aggregate calculation. Numerical results and comparable analysis illustrate the excellent performance of the EEMD-HGSA-MKLSSVM model when applied in the short-term wind speed forecasting.

scholarly journals Estimation of strawberry firmness using hyperspectral imaging: a comparison of regression models

2021 ◽  
Author(s):  
Binu Devassy ◽  
Sony George
Keyword(s):  
Hyperspectral Imaging ◽  
Regression Models ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Regression Techniques ◽  
Important Quality ◽  
Non Destructive ◽  
Better Than

Firmness is one of the most important quality measures of strawberries, and is related to other aspects of the fruit, such as flavour, ripeness and internal characteristics. The most popular method for measuring firmness is puncturing with a penetrometer, which is destructive and time-consuming. In the present study, we make an attempt to predict the firmness of strawberries in a fast, non-destructive and non-contact way using hyperspectral imaging (HSI) and data analysis with various regression techniques. The primary goal of this research is to investigate and compare the firmness prediction capability of seven prominent regression techniques. We have performed HSI data acquisition of 150 strawberries and optimised seven regression models using the spectral information to predict strawberry firmness. These models are linear, ridge, lasso, k-neighbours, random forest, support vector and partial least square regression. The res ults show that HSI data with regression models has the potential to predict firmness in a rapid, non-destructive manner. Out of these seven regression models, the k-neighbours regression model outperformed all other methods with a standard error of prediction of 0.14, which is better than that of the state-of-the-art results.

Noise model based ν-support vector regression with its application to short-term wind speed forecasting

2014 ◽  
Vol 57 ◽  
pp. 1-11 ◽  
Author(s):  
Qinghua Hu ◽  
Shiguang Zhang ◽  
Zongxia Xie ◽  
Jusheng Mi ◽  
Jie Wan
Keyword(s):  
Wind Speed ◽  
Support Vector Regression ◽  
Noise Model ◽  
Support Vector ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Model Based

scholarly journals Universal Impulse Noise Suppression Using Extended Efficient Nonparametric Switching Median Filter

2018 ◽  
Vol 214 ◽  
pp. 01003
Author(s):  
M. H. Suid ◽  
M. A. Ahmad ◽  
M. I. F. M. Hanif ◽  
M. Z. Tumari ◽  
M. S. Saealal
Keyword(s):  
Impulse Noise ◽  
Noise Suppression ◽  
Impulsive Noise ◽  
Median Filter ◽  
Noise Model ◽  
Restoration Technique ◽  
Switching Median Filter ◽  
Noise Models ◽  
Impulse Detector ◽  
Better Than

This paper presents a filtering algorithm called extended efficient nonparametric switching median (EENPSM) filter. The proposed filter is composed of a nonparametric easy to implement impulse noise detector and a recursive pixel restoration technique. Initially, the impulse detector classifies any possible impulsive noise pixels. Subsequently, the filtering phase replaces the detected noise pixels. In addition, the filtering phase employs fuzzy reasoning to deal with uncertainties present in local information. Contrary to the existing conventional filters that only focus on a particular impulse noise model, the EENPSM filter is capable of filtering all kinds of impulse noise (i.e. the random-valued and/or fixed-valued impulse noise models). Extensive qualitative and quantitative evaluations have shown that the EENPSM method performs better than some of the existing methods by giving better filtering performance.

scholarly journals Wind Speed and Solar Irradiance Prediction Using a Bidirectional Long Short-Term Memory Model Based on Neural Networks

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6501
Author(s):  
Fahad Radhi Alharbi ◽  
Denes Csala
Keyword(s):  
Neural Networks ◽  
Wind Speed ◽  
Solar Energy ◽  
Solar Irradiance ◽  
Short Term Memory ◽  
Historical Data ◽  
Power System Stability ◽  
System Stability ◽  
Short Term ◽  
Long Short Term Memory

The rapid growth of wind and solar energy penetration has created critical issues, such as fluctuation, uncertainty, and intermittence, that influence the power system stability, grid operation, and the balance of the power supply. Improving the reliability and accuracy of wind and solar energy predictions can enhance the power system stability. This study aims to contribute to the issues of wind and solar energy fluctuation and intermittence by proposing a high-quality prediction model based on neural networks (NNs). The most efficient technology for analyzing the future performance of wind speed and solar irradiance is recurrent neural networks (RNNs). Bidirectional RNNs (BRNNs) have the advantages of manipulating the information in two opposing directions and providing feedback to the same outputs via two different hidden layers. A BRNN’s output layer concurrently receives information from both the backward layers and the forward layers. The bidirectional long short-term memory (BI-LSTM) prediction model was designed to predict wind speed, solar irradiance, and ambient temperature for the next 169 h. The solar irradiance data include global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI). The historical data collected from Dumat al-Jandal City covers the period from 1 January 1985 to 26 June 2021, as hourly intervals. The findings demonstrate that the BI-LSTM model has promising performance in terms of evaluation, with considerable accuracy for all five types of historical data, particularly for wind speed and ambient temperature values. The model can handle different sizes of sequential data and generates low error metrics.

scholarly journals Accuracy of wind speed predictability with heights using Recurrent Neural networks

2021 ◽  
Vol 49 (4) ◽  
pp. 908-918
Author(s):  
M. Mohandes ◽  
S. Rehman ◽  
H. Nuha ◽  
M.S. Islam ◽  
F.H. Schulze
Keyword(s):  
Wind Speed ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Wind Power Integration ◽  
The Future ◽  
Speed Prediction ◽  
The Mean ◽  
Height Increase ◽  
Absolute Percent Error ◽  
Better Than

Accurate prediction of wind speed in future time domain is critical for wind power integration into the grid. Wind speed is usually measured at lower heights while the hub heights of modern wind turbines are much higher in the range of 80-120m. This study attempts to better understand the predictability of wind speed with height. To achieve this, wind data was collected using Laser Illuminated Detection and Ranging (LiDAR) system at 20m, 40m, 50m, 60m, 80m, 100m, 120m, 140m, 160m, and 180m heights. This hourly averaged data is used for training and testing a Recurrent Neural Network (RNN) for the prediction of wind speed for each of the future 12 hours, using 48 previous values. Detailed analyses of short-term wind speed prediction at different heights and future hours show that wind speed is predicted more accurately at higher heights.For example, the mean absolute percent error decreases from 0.19 to 0.16as the height increase from 20m to 180m, respectively for the 12 th future hour prediction. The performance of the proposed method is compared with Multilayer Perceptron (MLP) method. Results show that RNN performed better than MLP for most of the cases presented here at the future 6th hour.

scholarly journals Twin Least Square Support Vector Regression Model Based on Gauss-Laplace Mixed Noise Feature with Its Application in Wind Speed Prediction

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1102
Author(s):  
Shiguang Zhang ◽  
Chao Liu ◽  
Wei Wang ◽  
Baofang Chang
Keyword(s):  
Wind Speed ◽  
Least Squares ◽  
Support Vector Regression ◽  
Least Square ◽  
Support Vector ◽  
Lagrangian Multiplier ◽  
Data Set ◽  
Support Vector Regression Model ◽  
Proposed Model ◽  
Mixed Noise

In this article, it was observed that the noise in some real-world applications, such as wind power forecasting and direction of the arrival estimation problem, does not satisfy the single noise distribution, including Gaussian distribution and Laplace distribution, but the mixed distribution. Therefore, combining the twin hyperplanes with the fast speed of Least Squares Support Vector Regression (LS-SVR), and then introducing the Gauss–Laplace mixed noise feature, a new regressor, called Gauss-Laplace Twin Least Squares Support Vector Regression (GL-TLSSVR), for the complex noise. Subsequently, we apply the augmented Lagrangian multiplier method to solve the proposed model. Finally, we apply the short-term wind speed data-set to the proposed model. The results of this experiment confirm the effectiveness of our proposed model.

Sign in / Sign up

Export Citation Format

Share Document