Short-Term Wind Speed Forecasting Based on Optimizated Support Vector Machine

Sun, Yangyang; Li, Fulin; Huang, Xiao; Duan, Chao

doi:10.4028/www.scientific.net/amm.300-301.189

Cited by 7 publications

(5 citation statements)

References 6 publications

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“…The SVR model can map low dimensional nonlinear problems to high dimensional space [ 27 ], and transform them into linear problems. The optimal hyperplane can be found as follows

…”

Section: Mathematical Modelsmentioning

confidence: 99%

Wind Speed Prediction Based on Error Compensation

Jiao

Zhang

Wang

et al. 2023

Sensors

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Wind speed prediction is very important in the field of wind power generation technology. It is helpful for increasing the quantity and quality of generated wind power from wind farms. By using univariate wind speed time series, this paper proposes a hybrid wind speed prediction model based on Autoregressive Moving Average-Support Vector Regression (ARMA-SVR) and error compensation. First, to explore the balance between the computation cost and the sufficiency of the input features, the characteristics of ARMA are employed to determine the number of historical wind speeds for the prediction model. According to the selected number of input features, the original data are divided into multiple groups that can be used to train the SVR-based wind speed prediction model. Furthermore, in order to compensate for the time lag introduced by the frequent and sharp fluctuations in natural wind speed, a novel Extreme Learning Machine (ELM)-based error correction technique is developed to decrease the deviations between the predicted wind speed and its real values. By this means, more accurate wind speed prediction results can be obtained. Finally, verification studies were conducted by using real data collected from actual wind farms. Comparison results demonstrate that the proposed method can achieve better prediction results than traditional approaches.

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“…The SVR model can map low dimensional nonlinear problems to high dimensional space [ 27 ], and transform them into linear problems. The optimal hyperplane can be found as follows

…”

Section: Mathematical Modelsmentioning

confidence: 99%

Wind Speed Prediction Based on Error Compensation

Jiao

Zhang

Wang

et al. 2023

Sensors

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“…While this method, which is based on EMD, can improve forecasting results EMD has an end effect and over-enveloping problem. Sun [10] suggested local mean decomposition (LMD) and improved least-square support machine (LSSVM) to predict short-term wind speed, which can improve the modeling accuracy of LSSVM. However, the ability of the LMD algorithm to judge the FM signal needs to be tested.…”

Section: Introductionmentioning

confidence: 99%

Ultra-Short-Term Wind Speed Forecasting Using the Hybrid Model of Subseries Reconstruction and Broad Learning System

Pang

Zhang

et al. 2022

Energies

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The traditional decomposition–combination wind speed forecasting model has high complexity and a long calculation time. As a result, an ultra-short-term wind speed hybrid forecasting model based on a broad learning system (BLS) that combines improved variational mode decomposition (EPSO-VMD, EVMD) and subseries reconstruction (SR) is proposed in this work. The values of K and α in the EVMD are determined by minimum mean envelope entropy (MMEE) and enhanced particle swarm optimization (EPSO), and EVMD is used to decompose the original wind speed data. SR is applied to recombine the subseries obtained by EVMD to improve the forecasting efficiency. The sample entropy (SE) is used to quantify the subseries’ complexity, and they are then adaptively divided into high-entropy and low-entropy subseries. Adjacent high-entropy subseries of approximate entropy values are merged to obtain a new group of reconstructed high-entropy subseries, while the low-entropy subseries merge into a new subseries as well. Then, the forecasting results of the reconstructed high- and low-entropy subseries are calculated via the BLS and ARIMA models. Numerical simulation results show that the proposed method is more effective than traditional methods.

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“…In some of the earlier studies, wavelet has been adopted for the processing of the WS data by considering the same as a signal, while in some other cases a wavelet-based kernel has been used in the SVM model. Some of the studies which fall under the former category have been performed by Liu et al (2014), Sangita and Deshmukh (2011), Sun et al(2013), Sivanagaraja et al (2014), andPrasetyowati et al (2019).…”

Section: Introductionmentioning

confidence: 99%

Wavelet kernel least square twin support vector regression for wind speed prediction

Hazarika

Gupta

Natarajan³

2022

Environ Sci Pollut Res

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Wind energy is a potent yet freely available renewable energy. It is essential to estimate the wind speed (WS)precisely to makeaprecise estimation of wind power at wind power generating stations.Generally, the WS data is non-stationary. Wavelets have the potential to deal with the non-stationarilyindatasets. On the other hand, the prediction ability of primal least square support vector regression (PLSTSVR) has never been tested to best of our knowledge for WS prediction. Hence, in this work, wavelet kernel-based LSTSVR models are proposed for WS prediction. They are Morlet wavelet kernel LSTSVR and Mexican Hat wavelet kernel LSTSVR.HourlyWS data are collected from four different stations namely Chennai, Madurai, Salem and Tirunelveli in Tamil Nadu, India. The performance of the proposed models isevaluated using root mean square, mean absolute, symmetric mean absolute percentage, mean absolute scaled error and R 2 . The results of the proposed models are compared with twin support vector regression (TSVR), PLSTSVR and large-margin distribution machine-based regression (LDMR). Based on the results of the performance indicators, the performance of the proposed models is better when compared to other models.

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Short-Term Wind Speed Forecasting Based on Optimizated Support Vector Machine

Cited by 7 publications

References 6 publications

Wind Speed Prediction Based on Error Compensation

Wind Speed Prediction Based on Error Compensation

Ultra-Short-Term Wind Speed Forecasting Using the Hybrid Model of Subseries Reconstruction and Broad Learning System

Wavelet kernel least square twin support vector regression for wind speed prediction

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