Short-term wind speed prediction in wind farms based on banks of support vector machines

Wind speed prediction is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speed accurately is difficult. Aims at this challenge, a backtracking search optimization–based least squares support vector machine model is proposed for short-term wind speed prediction. In this article, the least squares support vector machine is chosen as the short-term wind speed prediction model and backtracking search optimization algorithm is used to optimize the important parameters which influence the least squares support vector machine regression model. Furthermore, the optimal parameters of the model are obtained, and the short-term wind speed prediction model of least squares support vector machine is established through parameter optimization. For time-varying systems similar to short-term wind speed time series, a model updating method based on prediction error accuracy combined with sliding window strategy is proposed. When the prediction model does not match the actual short-term wind model, least squares support vector machine trains and re-establishes. This model updating method avoids the mismatch problem between prediction model and actual wind speed data. The actual collected short-term wind speed time series is used as the research object. Multi-step prediction simulation of short-term wind speed is carried out. The simulation results show that backtracking search optimization algorithm–based least squares support vector machine model has higher prediction accuracy and reliability for the short-term wind speed. At the same time, the prediction performance indicators are also improved. The prediction result is that root mean square error is 0.1248, mean absolute error is 0.1374, mean absolute percentile error is 0.1589% and R2 is 0.9648. When the short-term wind speed varies from 0 to 4 m/s, the average value of absolute prediction error is 0.1113 m/s, and average value of absolute relative prediction error is 8.7111%. The proposed prediction model in this article has high engineering application value.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.511-512.927 ◽

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.

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

Mathematical Problems in Engineering ◽

10.1155/2018/9287097 ◽

2018 ◽

Vol 2018 ◽

pp. 1-21 ◽

Cited By ~ 3

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.

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Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

Advances in Meteorology ◽

10.1155/2017/6856139 ◽

2017 ◽

Vol 2017 ◽

pp. 1-22 ◽

Cited By ~ 7

Author(s):

Aiqing Kang ◽

Qingxiong Tan ◽

Xiaohui Yuan ◽

Xiaohui Lei ◽

Yanbin Yuan

Keyword(s):

Wind Speed ◽

Empirical Mode Decomposition ◽

Hybrid Model ◽

Superposition Principle ◽

Support Vector ◽

Short Term ◽

Wind Speed Forecasting ◽

Wind Speed Prediction ◽

Mode Decomposition ◽

Speed Prediction

Hybrid Ensemble Empirical Mode Decomposition (EEMD) and Least Square Support Vector Machine (LSSVM) is proposed to improve short-term wind speed forecasting precision. The EEMD is firstly utilized to decompose the original wind speed time series into a set of subseries. Then the LSSVM models are established to forecast these subseries. Partial autocorrelation function is adopted to analyze the inner relationships between the historical wind speed series in order to determine input variables of LSSVM models for prediction of every subseries. Finally, the superposition principle is employed to sum the predicted values of every subseries as the final wind speed prediction. The performance of hybrid model is evaluated based on six metrics. Compared with LSSVM, Back Propagation Neural Networks (BP), Auto-Regressive Integrated Moving Average (ARIMA), combination of Empirical Mode Decomposition (EMD) with LSSVM, and hybrid EEMD with ARIMA models, the wind speed forecasting results show that the proposed hybrid model outperforms these models in terms of six metrics. Furthermore, the scatter diagrams of predicted versus actual wind speed and histograms of prediction errors are presented to verify the superiority of the hybrid model in short-term wind speed prediction.

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Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm

Renewable Energy ◽

10.1016/j.renene.2013.08.011 ◽

2014 ◽

Vol 62 ◽

pp. 592-597 ◽

Cited By ~ 229

Author(s):

Da Liu ◽

Dongxiao Niu ◽

Hui Wang ◽

Leilei Fan

Keyword(s):

Genetic Algorithm ◽

Wavelet Transform ◽

Support Vector Machines ◽

Wind Speed ◽

Support Vector ◽

Short Term ◽

Wind Speed Forecasting ◽

Vector Machines

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Sample Selection Based on Active Learning for Short-Term Wind Speed Prediction

Energies ◽

10.3390/en12030337 ◽

2019 ◽

Vol 12 (3) ◽

pp. 337 ◽

Cited By ~ 3

Author(s):

Jian Yang ◽

Xin Zhao ◽

Haikun Wei ◽

Kanjian Zhang

Keyword(s):

Wind Speed ◽

Active Learning ◽

Wind Farm ◽

Sample Selection ◽

Support Vector ◽

Short Term ◽

Learning Methods ◽

Wind Speed Prediction ◽

Training Samples ◽

Speed Prediction

Wind speed prediction is the key to wind power prediction, which is very important to guarantee the security and stability of the power system. Due to dramatic changes in wind speed, it needs high-frequency sampling to describe the wind. A large number of samples are generated and affect modeling time and accuracy. Therefore, two novel active learning methods with sample selection are proposed for short-term wind speed prediction. The main objective of active learning is to minimize the number of training samples and ensure the prediction accuracy. In order to verify the validity of the proposed methods, the results of support vector regression (SVR) and artificial neural network (ANN) models with different training sets are compared. The experimental data are from a wind farm in Jiangsu Province. The simulation results show that the two novel active learning methods can effectively select typical samples. While reducing the number of training samples, the prediction performance remains almost the same or slightly improved.

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