A Novel Hybrid Model Based on an Improved Seagull Optimization Algorithm for Short-Term Wind Speed Forecasting

Wind energy is a clean energy source and is receiving widespread attention. Improving the operating efficiency and economic benefits of wind power generation systems depends on more accurate short-term wind speed predictions. In this study, a new hybrid model for short-term wind speed forecasting is proposed. The model combines variational modal decomposition (VMD), the proposed improved seagull optimization algorithm (ISOA) and the kernel extreme learning machine (KELM) network. The model adopts a hybrid modeling strategy: firstly, VMD decomposition is used to decompose the wind speed time series into several wind speed subseries. Secondly, KELM optimized by ISOA is used to predict each decomposed subseries. The ISOA technique is employed to accurately find the best parameters in each KELM network such that the predictability of a single KELM model can be enhanced. Finally, the prediction results of the wind speed sublayer are summarized to obtain the original wind speed. This hybrid model effectively characterizes the nonlinear and nonstationary characteristics of wind speed and greatly improves the forecasting performance. The experiment results demonstrate that: (1) the proposed VMD-ISOA-KELM model obtains the best performance for the application of three different prediction horizons compared with the other classic individual models, and (2) the proposed hybrid model combining the VMD technique and ISOA optimization algorithm performs better than models using other data preprocessing techniques.

Download Full-text

A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting

Applied Energy ◽

10.1016/j.apenergy.2019.03.097 ◽

2019 ◽

Vol 241 ◽

pp. 519-539 ◽

Cited By ~ 52

Author(s):

Xinsong Niu ◽

Jiyang Wang

Keyword(s):

Wind Speed ◽

Optimization Algorithm ◽

Data Preprocessing ◽

Multi Objective Optimization ◽

Short Term ◽

Combined Model ◽

Wind Speed Forecasting ◽

Multi Objective ◽

Model Based

Download Full-text

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.

Download Full-text