scholarly journals Short-term wind speed prediction based on CEEMDAN-SE-improved PIO-GRNN model

2020 ◽  
pp. 002029402098140
Author(s):  
Jiale Ding ◽  
Guochu Chen ◽  
Yongmin Huang ◽  
Zhiquan Zhu ◽  
Kuo Yuan ◽  
...  
Keyword(s):  
Wind Speed ◽  
Prediction Model ◽  
Prediction Models ◽  
Mean Squared Error ◽  
General Regression Neural Network ◽  
Percentage Error ◽  
Short Term ◽  
Prediction Ability ◽  
Wind Speed Prediction ◽  
Speed Prediction

In this paper, a short-term wind speed prediction model, called CEEMDAN-SE-Improved PIO-GRNN, is proposed to optimize the accuracy of the short-term wind speed forecast. This model is established on account of the optimized General Regression Neural Network (GRNN) method optimized by three algorithms, which are Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Sample Entropy (SE), and Pigeon Inspired Optimization (PIO), separately. Firstly, decomposing the original wind speed sequences into several subsequences with different complexity by CEEMDAN. Then, the complexity of each subsequence is judged by SE and the similar subsequences are combined into a new sequence to reduce the scale of calculation. Afterwards, the GRNN model optimized by improved PIO is used to predict each new sequence. Finally, the predicted results are superposed as the eventual predicted value. Implementing the prediction for the wind speed data of a wind field in north China within 30 days by applying the different prediction models, namely, GRNN, CEEMDAN-GRNN, Improved PIO-GRNN, and CEEMDAN-SE-Improved PIO-GRNN which are proposed in this paper. Comparing the prediction curves of different models with the fitting curve of the actual wind speed shows that the optimal fitting effect and minimum error value are included in CEEMDAN-SE-Improved PIO-GRNN model. Specifically, the values of mean squared error (MSE), mean absolute error (MAE) and weighted mean absolute percentage error (WMAPE) separately decrease by 0.6222, 0.3334, and 8.5766%, which compare with the single prediction model GRNN. Meanwhile, diebold-mariano (DM) test shows that the prediction ability of the two models is significantly different. The above statements indicate the proposed model does great advance in the precision of short-term wind speed prediction.

An application of backtracking search optimization–based least squares support vector machine for prediction of short-term wind speed

2019 ◽  
Vol 44 (3) ◽  
pp. 266-281 ◽  
Author(s):  
Zhongda Tian ◽  
Yi Ren ◽  
Gang Wang
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Prediction Model ◽  
Least Squares ◽  
Support Vector ◽  
Short Term ◽  
Backtracking Search ◽  
Wind Speed Prediction ◽  
Search Optimization ◽  
Speed Prediction

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.

Short-term wind speed prediction model based on GA-ANN improved by VMD

2020 ◽  
Vol 156 ◽  
pp. 1373-1388 ◽  
Author(s):  
Yagang Zhang ◽  
Guifang Pan ◽  
Bing Chen ◽  
Jingyi Han ◽  
Yuan Zhao ◽  
...  
Keyword(s):  
Wind Speed ◽  
Prediction Model ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Model Based ◽  
Speed Prediction

scholarly journals SHORT TERM WIND SPEED PREDICTION USING A NEW HYBRID MODEL WITH PASSIVE CONGREGATION

2012 ◽  
Vol 3 (2) ◽  
pp. 211-217 ◽  
Author(s):  
Tarek Abdelwahab Aboueldahab
Keyword(s):  
Wind Speed ◽  
Prediction Models ◽  
Hybrid Genetic Algorithm ◽  
Search Space ◽  
Short Term ◽  
Alternative Source ◽  
Mutual Cooperation ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Passive Congregation

Short term wind speed predicting is essential in using wind energy as an alternative source of electrical power generation, thus the improvement of wind speed prediction accuracy becomes an important issue. Although many prediction models have been developed during the last few years, they suffer a poor performance because their dependency on performing only the local search without the capability in performing the global search in the whole search space. To overcome this problem, we propose a new passive congregation term to the standard hybrid Genetic Algorithm / Particle Swarm Optimization (GA/ PSO) model in training Neural Network (NN) wind speed predictor. This term is based on the mutual cooperation between different particles in determining new positions rather than their selfish thinking. Experiment study shows significantly the influence of the passive congregation   term in improving the performance accuracy compared to the standard model.

Short-Term Wind Speed Prediction on Base of Improved Least Squares Support Vector Machine

2014 ◽  
Vol 599-601 ◽  
pp. 1972-1975
Author(s):  
Zheng Zhao ◽  
Long Xin Zhang ◽  
Hai Tao Liu ◽  
Zi Rui Liu
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Prediction Model ◽  
Least Squares ◽  
Wind Farm ◽  
Support Vector ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Neural Network Prediction ◽  
Speed Prediction

Accurate wind speed prediction is of significance to improve the ability to coordinate operation of a wind farm with a power system and ensure the safety of power grid operation. According to the randomness and volatility of wind speed, it is put forward that a WD_GA_LS_SVM short-term wind speed combination prediction model on basis of Wavelet decomposition (WD), Genetic alogorithms (GA) optimization and Least squares support vector machine (LS_SVM). Short-term wind speed prediction is carried out and compared with the neural network prediction model with use of the measured data of a wind farm. The results of error analysis indicate the combination prediction model selected is of higher prediction accuracy.

scholarly journals Deterministic and Interval Wind Speed Prediction Method in Offshore Wind Farm Considering the Randomness of Wind

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5595
Author(s):  
Qin Chen ◽  
Yan Chen ◽  
Xingzhi Bai
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Hybrid Model ◽  
Stationary Time Series ◽  
Percentage Error ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Interval Prediction ◽  
Stationary Time ◽  
Speed Prediction

In order to improve the prediction accuracy of wind speed, this paper proposes a hybrid wind speed prediction (WSP) method considering the fluctuation, randomness and nonlinear of wind, which can be applied to short-term deterministic and interval prediction. Variational mode decomposition (VMD) decomposes wind speed time series into nonlinear series Intrinsic mode function 1 (IMF1), stationary time series IMF2 and error sreies (ER). Principal component analysis-Radial basis function (PCA-RBF) model is used to model the nonlinear series IMF1, where PCA is applied to reduce the redundant information. Long short-term memory (LSTM) is used to establish a stationary time series model for IMF2, which can better describe the fluctuation trend of wind speed; mixture Gaussian process regression (MGPR) is used to predict ER to obtain deterministic and interval prediction results simultaneously. Finally, above methods are reconstructed to form VMD-PRBF-LSTM-MGPR which is the abbreviation of hybrid model to obtain the final results of WSP, which can better reflect the volatility of wind speed. Nine comparison models are built to verify the availability of the hybrid model. The mean absolute percentage error (MAE) and mean square error (MSE) of deterministic WSP of the proposed model are only 0.0713 and 0.3158 respectively, which are significantly smaller than the prediction results of comparison models. In addition, confidence intervals (CIs) and prediction interval (PIs) are compared in this paper. The experimental results show that both of them can quantify and represent forecast uncertainty and the PIs is wider than the corresponding CIs.

scholarly journals Fractional Levy Stable and Maximum Lyapunov Exponent for Wind Speed Prediction

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 605
Author(s):  
Shouwu Duan ◽  
Wanqing Song ◽  
Carlo Cattani ◽  
Yakufu Yasen ◽  
He Liu
Keyword(s):  
Lyapunov Exponent ◽  
Wind Speed ◽  
Prediction Model ◽  
Prediction Models ◽  
Similarity Index ◽  
Maximum Lyapunov Exponent ◽  
Range Analysis ◽  
Wind Speed Prediction ◽  
Rescaled Range Analysis ◽  
Speed Prediction

In this paper, a wind speed prediction method was proposed based on the maximum Lyapunov exponent (Le) and the fractional Levy stable motion (fLsm) iterative prediction model. First, the calculation of the maximum prediction steps was introduced based on the maximum Le. The maximum prediction steps could provide the prediction steps for subsequent prediction models. Secondly, the fLsm iterative prediction model was established by stochastic differential. Meanwhile, the parameters of the fLsm iterative prediction model were obtained by rescaled range analysis and novel characteristic function methods, thereby obtaining a wind speed prediction model. Finally, in order to reduce the error in the parameter estimation of the prediction model, we adopted the method of weighted wind speed data. The wind speed prediction model in this paper was compared with GA-BP neural network and the results of wind speed prediction proved the effectiveness of the method that is proposed in this paper. In particular, fLsm has long-range dependence (LRD) characteristics and identified LRD by estimating self-similarity index H and characteristic index α. Compared with fractional Brownian motion, fLsm can describe the LRD process more flexibly. However, the two parameters are not independent because the LRD condition relates them by αH > 1.

scholarly journals Multi-Step Short-Term Wind Speed Prediction Using a Residual Dilated Causal Convolutional Network with Nonlinear Attention

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1772 ◽  
Author(s):  
Kumar Shivam ◽  
Jong-Chyuan Tzou ◽  
Shang-Chen Wu
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Convolutional Neural Network ◽  
Real World ◽  
Prediction Models ◽  
Short Term Memory ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Proposed Model ◽  
Speed Prediction

Wind energy is the most used renewable energy worldwide second only to hydropower. However, the stochastic nature of wind speed makes it harder for wind farms to manage the future power production and maintenance schedules efficiently. Many wind speed prediction models exist that focus on advance neural networks and/or preprocessing techniques to improve the accuracy. Since most of these models require a large amount of historic wind data and are validated using the data split method, the application to real-world scenarios cannot be determined. In this paper, we present a multi-step univariate prediction model for wind speed data inspired by the residual U-net architecture of the convolutional neural network (CNN). We propose a residual dilated causal convolutional neural network (Res-DCCNN) with nonlinear attention for multi-step-ahead wind speed forecasting. Our model can outperform long-term short-term memory networks (LSTM), gated recurrent units (GRU), and Res-DCCNN using sliding window validation techniques for 50-step-ahead wind speed prediction. We tested the performance of the proposed model on six real-world wind speed datasets with different probability distributions to confirm its effectiveness, and using several error metrics, we demonstrated that our proposed model was robust, precise, and applicable to real-world cases.

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