Wind Speed Forecasting Based on EMD and GRNN Optimized by FOA

Niu, Dongxiao; Liang, Yi; Hong, Wei‐Chiang

doi:10.3390/en10122001

Cited by 44 publications

(33 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhao et al [30] analyzed the characteristics of the outliers caused by wind curtailment, then, a data-driven outlier elimination approach, combining quartile method and density-based clustering method was proposed; however, variational mode Energies 2018, 11, 697 3 of 23 decomposition (VMD) is prone to mode mixing problems. Niu et al [31] used empirical mode decomposition (EMD) to decompose original wind speed data, then, a novel hybrid forecasting model based on the general regression neural network (GRNN) method, optimized by the fruit fly optimization algorithm (FOA), was proposed. Ye et al [32] discussed EMD, EEMD, complementary ensemble empirical mode decomposition (CEEMD), and complete empirical mode decomposition with adaptive noise (CEEMDAN).…”

Section: Introductionmentioning

confidence: 99%

“…Zhao et al [30] analyzed the characteristics of the outliers caused by wind curtailment, then, a data-driven outlier elimination approach, combining quartile method and density-based clustering method was proposed; however, variational mode decomposition (VMD) is prone to mode mixing problems. Niu et al [31] used empirical mode decomposition (EMD) to decompose original wind speed data, then, a novel Wind power forecasting is difficult to achieve due to its intermittency and stochastic fluctuation, which brings great challenges to power system operation and control [5,6]. Over the past few decades, a large amount of research has been devoted to the development of effective and reliable wind speed/power forecasting methods, models, and tools [7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA

Lin

Sun

et al. 2018

Energies

View full text Add to dashboard Cite

Abstract:Wind power time series data always exhibits nonlinear and non-stationary features, making it very difficult to accurately predict. In this paper, a novel hybrid wind power time series prediction model, based on ensemble empirical mode decomposition-permutation entropy (EEMD-PE), the least squares support vector machine model (LSSVM), and gravitational search algorithm (GSA), is proposed to improve accuracy of ultra-short-term wind power forecasting. To process the data, original wind power series were decomposed by EEMD-PE techniques into a number of subsequences with obvious complexity differences. Then, a new heuristic GSA algorithm was utilized to optimize the parameters of the LSSVM. The optimized model was developed for wind power forecasting and improved regression prediction accuracy. The proposed model was validated with practical wind power generation data from the Hebei province, China. A comprehensive error metric analysis was carried out to compare the performance of our method with other approaches. The results showed that the proposed model enhanced forecasting performance compared to other benchmark models.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Zhao et al [30] analyzed the characteristics of the outliers caused by wind curtailment, then, a data-driven outlier elimination approach, combining quartile method and density-based clustering method was proposed; however, variational mode decomposition (VMD) is prone to mode mixing problems. Niu et al [31] used empirical mode decomposition (EMD) to decompose original wind speed data, then, a novel Wind power forecasting is difficult to achieve due to its intermittency and stochastic fluctuation, which brings great challenges to power system operation and control [5,6]. Over the past few decades, a large amount of research has been devoted to the development of effective and reliable wind speed/power forecasting methods, models, and tools [7].…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA

Lin

Sun

et al. 2018

Energies

View full text Add to dashboard Cite

show abstract

“…The predetermined parameters of the window length l for Hankel matrix in SSA is set as 500. The parameters (K, α, γ) in VMD, (s) in SSA, (τ, d) in PSR, and (C, σ 2 ) in KELM are searched for the scopes of [2,10], [0, 1], [1,2000], [1,167], [1,15], [1,40], [1,1000], and [1,1000], orderly. For the relevant comparative models, the regularization coefficient C and the kernel parameter σ 2 of all the SVR-and KELM-based models are optimized by GS, where the searching scopes are in intervals [2 −8 , 2 8 ] and [2 −5 , 2 5 ], respectively.…”

Section: Experimental Descriptionmentioning

confidence: 99%

“…The superior performance in dealing with nonlinear and non-stationary time series has been approved by a number of scholars. Among the varied methods, artificial neural networks (ANNs) [9,10] possess strong robustness, as well as the ability to fully approximate complex nonlinear relationships, whereas the network structures are difficult to determine, as well as being time consuming for examination. In contrast, the appropriate parameters in support vector regression (SVR) [11,12] are easier to determine, of which the nonlinear forecasting problems could be solved by proper kernel transformations.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for Multi-Step Wind Speed Forecasting Based on Multi-Scale Dominant Ingredient Chaotic Analysis, KELM and Synchronous Optimization Strategy

Wang

Zhou

et al. 2019

Sustainability

View full text Add to dashboard Cite

Accurate wind speed prediction plays a significant role in reasonable scheduling and the safe operation of the power system. However, due to the non-linear and non-stationary traits of the wind speed time series, the construction of an accuracy forecasting model is difficult to achieve. To this end, a novel synchronous optimization strategy-based hybrid model combining multi-scale dominant ingredient chaotic analysis and a kernel extreme learning machine (KELM) is proposed, for which the multi-scale dominant ingredient chaotic analysis integrates variational mode decomposition (VMD), singular spectrum analysis (SSA) and phase-space reconstruction (PSR). For such a hybrid structure, the parameters in VMD, SSA, PSR and KELM that would affect the predictive performance are optimized by the proposed improved hybrid grey wolf optimizer-sine cosine algorithm (IHGWOSCA) synchronously. To begin with, VMD is employed to decompose the raw wind speed data into a set of sub-series with various frequency scales. Later, the extraction of dominant and residuary ingredients for each sub-series is implemented by SSA, after which, all of the residuary ingredients are accumulated with the residual of VMD, to generate an additional forecasting component. Subsequently, the inputs and outputs of KELM for each component are deduced by PSR, with which the forecasting model could be constructed. Finally, the ultimate forecasting values of the raw wind speed are calculated by accumulating the predicted results of all the components. Additionally, four datasets from Sotavento Galicia (SG) wind farm have been selected, to achieve the performance assessment of the proposed model. Furthermore, six relevant models are carried out for comparative analysis. The results illustrate that the proposed hybrid framework, VMD-SSA-PSR-KELM could achieve a better performance compared with other combined models, while the proposed synchronous parameter optimization strategy-based model could achieve an average improvement of 25% compared to the separated optimized VMD-SSA-PSR-KELM model.

show abstract

“…Yuan [17] employed the ARIMA and Grey Model (1,1) model for China's primary energy consumption. To forecast wind speed, Niu [18] developed a novel hybrid forecasting model, combing empirical mode decomposition (EMD) and the general regression neural network (GRNN) optimized by the fruit fly optimization algorithm (FOA) to forecast wind speed.…”

Section: Literature Reviewmentioning

confidence: 99%

A New Hybrid Method for China’s Energy Supply Security Forecasting Based on ARIMA and XGBoost

Пин

Zhang

2018

Energies

View full text Add to dashboard Cite

Abstract:Energy supply security is a significant part of China's security, directly influencing national security and economic and social sustainability. To ensure both China's present and the future energy supply, it is essential to evaluate and forecast the energy supply level. However, forecasting the energy supply security level is difficult because energy supply security is dynamic, many factors affect it and there is a lack of accurate and comprehensive data. Therefore, based on previous studies and according to the characteristics of energy supply and the social development of China, first, the authors apply a comprehensive evaluation method to quantify the energy supply security. Second, based on the ARIMA-XGBoost hybrid model, the authors create two novel approaches for forecasting the energy supply security level of China. The authors find that: (1) energy supply security is dynamic, and green development has become the theme of China's energy development. The energy industry urgently needs to provide more high-quality ecological energy products to meet the people's desire for a beautiful ecological environment; (2) since the mean absolute percentage errors are below 4.5% when forecasting the energy supply security indicators, the ARIMA-XGBoost hybrid model is more accurate for forecasting China's energy supply security level and (3) the security level of China's energy supply has developed periodic features; the ESSI can improve by about 0.2 every five years, but, due to the low starting point and multiple types of constraints, it is difficult to reach the safety level in a short time.

show abstract

Wind Speed Forecasting Based on EMD and GRNN Optimized by FOA

Cited by 44 publications

References 37 publications

A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA

A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA

A Hybrid Approach for Multi-Step Wind Speed Forecasting Based on Multi-Scale Dominant Ingredient Chaotic Analysis, KELM and Synchronous Optimization Strategy

A New Hybrid Method for China’s Energy Supply Security Forecasting Based on ARIMA and XGBoost

Contact Info

Product

Resources

About