Wind speed prediction using reduced support vector machines with feature selection

Kong, Xiaobing; Liu, Xiangjie; Shi, Ruifeng; Lee, Kwang Y.

doi:10.1016/j.neucom.2014.09.090

Cited by 153 publications

(52 citation statements)

References 10 publications

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“…A Back Propagation (BP) neural network on account of Particle Swarm Optimization (PSO) has been conducted for input parameter selection to decrease errors in wind speed prediction [30]. A reduced support vector machine (RSVM) optimized by PSO has been utilized to conduct wind speed forecasting with feature selection [31]. Additionally, recurrent neural networks [32], Multi-Layer Perceptron (MLP) neural networks [17], and RBF neural networks [33] have been put forward for the prediction of wind power.…”

Section: Statistical Forecasting Approachmentioning

confidence: 99%

Short-Term Wind Electric Power Forecasting Using a Novel Multi-Stage Intelligent Algorithm

Zhao

Guo

2018

Sustainability

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Abstract:As the most efficient renewable energy source for generating electricity in a modern electricity network, wind power has the potential to realize sustainable energy supply. However, owing to its random and intermittent instincts, a high permeability of wind power into a power network demands accurate and effective wind energy prediction models. This study proposes a multi-stage intelligent algorithm for wind electric power prediction, which combines the Beveridge-Nelson (B-N) decomposition approach, the Least Square Support Vector Machine (LSSVM), and a newly proposed intelligent optimization approach called the Grasshopper Optimization Algorithm (GOA). For data preprocessing, the B-N decomposition approach was employed to disintegrate the hourly wind electric power data into a deterministic trend, a cyclic term, and a random component. Then, the LSSVM optimized by the GOA (denoted GOA-LSSVM) was applied to forecast the future 168 h of the deterministic trend, the cyclic term, and the stochastic component, respectively. Finally, the future hourly wind electric power values can be obtained by multiplying the forecasted values of these three trends. Through comparing the forecasting performance of this proposed method with the LSSVM, the LSSVM optimized by the Fruit-fly Optimization Algorithm (FOA-LSSVM), and the LSSVM optimized by Particle Swarm Optimization (PSO-LSSVM), it is verified that the established multi-stage approach is superior to other models and can increase the precision of wind electric power prediction effectively.

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Section: Statistical Forecasting Approachmentioning

confidence: 99%

Short-Term Wind Electric Power Forecasting Using a Novel Multi-Stage Intelligent Algorithm

Zhao

Guo

2018

Sustainability

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“…Recently a comparison of the performance of the SVM with other relevant neural network topologies has been carried out for contingency ranking and classification in [8]. SVMs are applied for classification of power quality events [9], multi-dimensional data classification [10], classification of microarrays [11], wind speed prediction [12], voltage stability monitoring [13] and many more [14]. The main reason behind this popularity of the SVM as a classifier is that SVM can handle large feature space.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Electric Price Forecast using Interbreed Approach of Linear Regression and SVM

Saini

Saxena

2016

IJEECS

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“…Wind speeds are random and fluctuate significantly; therefore, accurate short-term wind speed forecasting is difficult. Methods based on time series [1][2][3][4] and machine learning (ML) [5][6][7][8][9][10] have been widely used to construct wind speed forecasting models. Because of their high forecasting accuracy and ability to generalize, Traditional ML methods such as neural networks (NN) and support vector machines (SVM) have become a research focus in recent years.…”

Section: Introductionmentioning

confidence: 99%

“…Currently, many feature selection methods have been introduced in wind speed forecasting research. Principal component analysis, as a traditional dimensionality reduction method, is utilized to determine the major factors affecting the wind speed [9]. At the same time, partial autocorrelation function [4,8], phase space reconstruction [10], granger causality test [8], coral reefs optimization [12] and other methods were validated successfully in the input selection.…”

Section: Introductionmentioning

confidence: 99%

Using Random Forests to Select Optimal Input Variables for Short-Term Wind Speed Forecasting Models

Wang

Sun

et al. 2017

Energies

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Achieving relatively high-accuracy short-term wind speed forecasting estimates is a precondition for the construction and grid-connected operation of wind power forecasting systems for wind farms. Currently, most research is focused on the structure of forecasting models and does not consider the selection of input variables, which can have significant impacts on forecasting performance. This paper presents an input variable selection method for wind speed forecasting models. The candidate input variables for various leading periods are selected and random forests (RF) is employed to evaluate the importance of all variable as features. The feature subset with the best evaluation performance is selected as the optimal feature set. Then, kernel-based extreme learning machine is constructed to evaluate the performance of input variables selection based on RF. The results of the case study show that by removing the uncorrelated and redundant features, RF effectively extracts the most strongly correlated set of features from the candidate input variables. By finding the optimal feature combination to represent the original information, RF simplifies the structure of the wind speed forecasting model, shortens the training time required, and substantially improves the model's accuracy and generalization ability, demonstrating that the input variables selected by RF are effective.

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Wind speed prediction using reduced support vector machines with feature selection

Cited by 153 publications

References 10 publications

Short-Term Wind Electric Power Forecasting Using a Novel Multi-Stage Intelligent Algorithm

Short-Term Wind Electric Power Forecasting Using a Novel Multi-Stage Intelligent Algorithm

Electric Price Forecast using Interbreed Approach of Linear Regression and SVM

Using Random Forests to Select Optimal Input Variables for Short-Term Wind Speed Forecasting Models

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