An improved Wavelet Transform using Singular Spectrum Analysis for wind speed forecasting based on Elman Neural Network

Yu, Chuanjin; Li, Yongle; Zhang, Mingjin

doi:10.1016/j.enconman.2017.05.063

Cited by 134 publications

(38 citation statements)

References 35 publications

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“…Liu et al [24] presented a novel hybrid model, with FEEMD and wavelet packet decomposition, and ENN, which had a desirable performance in multi-step ahead wind speed forecasting. Yu et al [25] designed a new hybrid model combining improved wavelet transform (IWT) and ENN, which exhibited satisfactory performance in wind speed forecasting. Although ENN has made many contributions in the field of wind power and wind speed prediction, wind power has intermittent and volatility characteristics.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Models for Wind Power Using Extreme-Point Symmetric Mode Decomposition and Artificial Neural Networks

Zhou

Huo

et al. 2019

Sustainability

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The randomness and volatility of wind power poses a serious threat to the stability, continuity, and adjustability of the power system when it is connected to the grid. Accurate short-term wind power prediction methods have important practical value for achieving high-precision prediction of wind farm power generation and safety and economic dispatch. Therefore, this paper proposes a novel combined model to improve the accuracy of short-term wind power prediction, which involves grey correlation degree analysis, ESMD (extreme-point symmetric mode decomposition), sample entropy (SampEn) theory, and a hybrid prediction model based on three prediction algorithms. The meteorological data at different times and altitudes is firstly selected as the influencing factors of wind power. Then, the wind power sub-series obtained by the ESMD method is reconstructed into three wind power characteristic components, namely PHC (high frequency component of wind power), PMC (medium frequency component of wind power), and PLC (low frequency component of wind power). Similarly, the wind speed sub-series obtained by the ESMD method is reconstructed into three wind speed characteristic components, called SHC (high frequency component of wind speed), SMC (medium frequency component of wind speed), and SLC (low frequency component of wind speed). Subsequently, the Bat-BP model, Adaboost-ENN model, and ENN (Elman neural network), which have high forecasting accuracy, are selected to predict PHC, PMC, and PLC, respectively. Finally, the prediction results of three characteristic components are aggregated into the final prediction values of the original wind power series. To evaluate the prediction performance of the proposed combined model, 15-min wind power and meteorological data from the wind farm in China are adopted as case studies. The prediction results show that the combined model shows better performance in short-term wind power prediction compared with other models.

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Section: Introductionmentioning

confidence: 99%

Forecasting Models for Wind Power Using Extreme-Point Symmetric Mode Decomposition and Artificial Neural Networks

Zhou

Huo

et al. 2019

Sustainability

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“…The introduction of the nonparametric time series analysis and forecasting technique of singular spectrum analysis (SSA) is closely associated with the work of Broomhead and King (, ). Since then, SSA has progressed rapidly and transformed itself into a powerful technique that is increasingly exploited for providing solutions to real‐world problems in a variety of different fields; see, for example, Gong, Song, He, Gong, and Ren (), Merte (), Yu, Li, and Zhang (), Mahmoudvand and Rodrigues (), Khan and Poskitt (), Hassani, Ghodsi, Silva, and Heravi (), Hassani, Silva, Antonakakis, Filis, and Gupta (), Hassani, Webster, Silva, and Heravi (), Lai and Guo (), Ghodsi, Silva, and Hassani (), and Silva and Hassani (). Few reasons underlying this augmented usage of SSA can be partly attributed to its nonparametric nature.…”

Section: Introductionmentioning

confidence: 99%

Modeling European industrial production with multivariate singular spectrum analysis: A cross‐industry analysis

Silva

Hassani

Heravi

2018

Journal of Forecasting

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In this paper, an optimized Multivariate Singular Spectrum Analysis (MSSA) approach is proposed to find leading indicators of cross industry relations between 24 monthly, seasonally unadjusted industrial production (IP) series for German, French and UK economies. Both Recurrent and Vector forecasting algorithms of Horizontal MSSA (HMSSA) are considered. The results from the proposed multivariate approach are compared with those obtained via the optimized univariate Singular Spectrum Analysis (SSA) forecasting algorithm to determine the statistical significance of each outcome. The data is rigorously tested for normality, seasonal unit root hypothesis and structural breaks. The results are presented such that users can not only identify the most appropriate model based on the aim of the analysis, but also easily identify the leading indicators for each IP variable in each country. Our findings show that for all three countries, forecasts from the proposed MSSA algorithm outperform the optimized SSA algorithm in over 70% of the cases. Accordingly, this new approach succeeds in identifying leading indicators and is a viable option for selecting the SSA choices L and r which minimises a loss function.

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“…Hu, Wang, and Xiao used the Empirical Wavelet Transform (EWT), Expectation Propagation (EP) algorithm and Gaussian process regression with the Student-t Observation Model (GPR-t) to study the dynamic characteristics of wind speed [20]. Yu, Li, and Zhang adopted Improved WT (IWT) and Singular Spectrum Analysis (SSA) to preprocess the options and designed a model combined with the IWT-ENN and Elman Neural Networks (ENN) [21]. Liu et al employed Wavelet Decomposition-WD, Wavelet Packet Decomposition-WPD, Empirical Mode Decomposition-EMD and Fast Ensemble Empirical Mode and Decomposition-FEEMD to decompose the influence factors, and proposed the model of FEEMD-MLP and FEEMD-ANFIS to improve the forecast precision of wind speed [22].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Wind Power Forecasting: A New Hybrid Model Combined Extreme-Point Symmetric Mode Decomposition, Extreme Learning Machine and Particle Swarm Optimization

Zhou

Jin

2018

Sustainability

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The nonlinear and non-stationary nature of wind power creates a difficult challenge for the stable operation of the power system when it accesses the grid. Improving the prediction accuracy of short-term wind power is beneficial to the power system dispatching department in formulating a power generation plan, reducing the rotation reserve capacity and improving the safety and reliability of the power grid operation. This paper has constructed a new hybrid model, named the ESMD-PSO-ELM model, which combines Extreme-point symmetric mode decomposition (ESMD), Extreme Learning Machine (ELM) and Particle swarm optimization (PSO). Firstly, the ESMD is applied to decompose wind power into several intrinsic mode functions (IMFs) and one residual(R). Then, the PSO-ELM is applied to predict each IMF and R. Finally, the predicted values of these components are assembled into the final forecast value compared with the original wind power. To verify the predictive performance of the proposed model, this paper selects actual wind power data from 1 April 2016 to 30 April 2016 with a total of 2880 observation values located in Yunnan, China for the experimental sample. The MAPE, NMAE and NRMSE values of the proposed model are 4.76, 2.23 and 2.70, respectively, and these values are lower than those of the other eight models. The empirical study demonstrates that the proposed model is more robust and accurate in forecasting short-term wind power compared with the other eight models.

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An improved Wavelet Transform using Singular Spectrum Analysis for wind speed forecasting based on Elman Neural Network

Cited by 134 publications

References 35 publications

Forecasting Models for Wind Power Using Extreme-Point Symmetric Mode Decomposition and Artificial Neural Networks

Forecasting Models for Wind Power Using Extreme-Point Symmetric Mode Decomposition and Artificial Neural Networks

Modeling European industrial production with multivariate singular spectrum analysis: A cross‐industry analysis

Short-Term Wind Power Forecasting: A New Hybrid Model Combined Extreme-Point Symmetric Mode Decomposition, Extreme Learning Machine and Particle Swarm Optimization

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