Wind speed forecasting models based on data decomposition, feature selection and group method of data handling network

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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A New Wind Speed Forecasting Modeling Strategy Using Two-Stage Decomposition, Feature Selection and DAWNN

Energies ◽

10.3390/en12030334 ◽

2019 ◽

Vol 12 (3) ◽

pp. 334 ◽

Cited By ~ 7

Author(s):

Sizhou Sun ◽

Lisheng Wei ◽

Jie Xu ◽

Zhenni Jin

Keyword(s):

Feature Selection ◽

Wind Speed ◽

Wind Farms ◽

Frequency Component ◽

Ensemble Empirical Mode Decomposition ◽

Two Stage ◽

Wind Speed Forecasting ◽

Wind Speed Data ◽

Wind Speed Prediction ◽

Mode Decomposition

Accurate wind speed prediction plays a crucial role on the routine operational management of wind farms. However, the irregular characteristics of wind speed time series makes it hard to predict accurately. This study develops a novel forecasting strategy for multi-step wind speed forecasting (WSF) and illustrates its effectiveness. During the WSF process, a two-stage signal decomposition method combining ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) is exploited to decompose the empirical wind speed data. The EEMD algorithm is firstly employed to disassemble wind speed data into several intrinsic mode function (IMFs) and one residual (Res). The highest frequency component, IMF1, obtained by EEMD is further disassembled into different modes by the VMD algorithm. Then, feature selection is applied to eliminate the illusive components in the input-matrix predetermined by partial autocorrelation function (PACF) and the parameters in the proposed wavelet neural network (WNN) model are optimized for improving the forecasting performance, which are realized by hybrid backtracking search optimization algorithm (HBSA) integrating binary-valued BSA (BBSA) with real-valued BSA (RBSA), simultaneously. Combinations of Morlet function and Mexican hat function by weighted coefficient are constructed as activation functions for WNN, namely DAWNN, to enhance its regression performance. In the end, the final WSF values are obtained by assembling the prediction results of each decomposed components. Two sets of actual wind speed data are applied to evaluate and analyze the proposed forecasting strategy. Forecasting results, comparisons, and analysis illustrate that the proposed EEMD/VMD-HSBA-DAWNN is an effective model when employed in multi-step WSF.

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A Hybrid Model Based on Ensemble Empirical Mode Decomposition and Fruit Fly Optimization Algorithm for Wind Speed Forecasting

Advances in Meteorology ◽

10.1155/2016/3768242 ◽

2016 ◽

Vol 2016 ◽

pp. 1-14 ◽

Cited By ~ 5

Author(s):

Zongxi Qu ◽

Kequan Zhang ◽

Jianzhou Wang ◽

Wenyu Zhang ◽

Wennan Leng

Keyword(s):

Wind Speed ◽

Wind Power ◽

Optimization Algorithm ◽

Fruit Fly ◽

Ensemble Empirical Mode Decomposition ◽

Fruit Fly Optimization Algorithm ◽

Fruit Fly Optimization ◽

Wind Speed Forecasting ◽

Forecasting Models ◽

Mode Decomposition

As a type of clean and renewable energy, the superiority of wind power has increasingly captured the world’s attention. Reliable and precise wind speed prediction is vital for wind power generation systems. Thus, a more effective and precise prediction model is essentially needed in the field of wind speed forecasting. Most previous forecasting models could adapt to various wind speed series data; however, these models ignored the importance of the data preprocessing and model parameter optimization. In view of its importance, a novel hybrid ensemble learning paradigm is proposed. In this model, the original wind speed data is firstly divided into a finite set of signal components by ensemble empirical mode decomposition, and then each signal is predicted by several artificial intelligence models with optimized parameters by using the fruit fly optimization algorithm and the final prediction values were obtained by reconstructing the refined series. To estimate the forecasting ability of the proposed model, 15 min wind speed data for wind farms in the coastal areas of China was performed to forecast as a case study. The empirical results show that the proposed hybrid model is superior to some existing traditional forecasting models regarding forecast performance.

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Comparative investigation of short-term wind speed forecasting models for airborne wind turbines

2017 8th International Conference on Mechanical and Aerospace Engineering (ICMAE) ◽

10.1109/icmae.2017.8038688 ◽

2017 ◽

Cited By ~ 1

Author(s):

Natapol Korprasertsak ◽

Thananchai Leephakpreeda

Keyword(s):

Wind Speed ◽

Wind Turbines ◽

Comparative Investigation ◽

Short Term ◽

Wind Speed Forecasting ◽

Forecasting Models

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Short-Term Wind Speed Forecasting Based on Low Redundancy Feature Selection

Energies ◽

10.3390/en11071638 ◽

2018 ◽

Vol 11 (7) ◽

pp. 1638 ◽

Cited By ~ 8

Author(s):

Nantian Huang ◽

Enkai Xing ◽

Guowei Cai ◽

Zhiyong Yu ◽

Bin Qi ◽

...

Keyword(s):

Feature Selection ◽

Wind Speed ◽

Short Term ◽

Wind Speed Forecasting

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Comparative models for multi-step ahead wind speed forecasting applied for expected wind turbine power output prediction

Wind Engineering ◽

10.1177/0309524x211052015 ◽

2021 ◽

pp. 0309524X2110520

Author(s):

Germaine Djuidje Kenmoé ◽

Hervice Roméo Fogno Fotso ◽

Claude Vidal Aloyem Kazé

Keyword(s):

Neural Networks ◽

Wind Speed ◽

Wind Turbine ◽

Arima Model ◽

Physical Method ◽

Computational Time ◽

Dynamic Neural Networks ◽

Wind Speed Forecasting ◽

Forecasting Models ◽

One Step

This paper investigates six of the most widely used wind speed forecasting models for a combination of statistical and physical methods for the purpose of Wind Turbine Power Generation (WTPG) prediction in Cameroon. Statistical method based on both single static and dynamic neural networks architectures and two hybrid neural networks architectures in comparison to ARIMA model are employed for multi-step ahead wind speed forecasting in two Datasets in Bapouh, Cameroon. The physical method is used to estimate 1 day ahead expected WTPG for each Dataset using the previous predicted wind speed from better forecasting models. The obtained results of multi-step ahead forecasting showed that the ARIMA and nonlinear autoregression with exogenous input neural network (NARXNN) models perform well the wind speed forecasting than other forecasting models in both Datasets. The better performances of ARIMA are achieved with one-step ahead and two-step ahead forecasting, while NARXNN is better with one-step ahead forecasting. But NARXNN models have more computational time than other models such as ARIMA models. Furthermore, the effectiveness of employed hybrid method for WTPG prediction is proven.

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