A Hybrid Short-Term Wind Power Prediction Model Combining Data Processing, Multiple Parameters Optimization and Multi-Intelligent Models Apportion Strategy

Abstract

As the wind power dates have strong volatility and intermittency, it is difficult to meet the safety and stability of power system operation. To make full use of the characteristics of wind power data and further improve wind power forecasting performance, an innovative hybrid framework is proposed to response to the above challenges, of which the non-stationary weakening technique, sample entropy (SE)-based prediction model allocation, optimized reduced kernel extreme learning machine (RKELM) and a novel deep learning network are integrated befittingly. Initially, the original wind power sequence is decomposed into multiple sub-sequences by empirical wavelet transform (EWT) adaptively, after which SE is employed to analyse the high and low components of sub-sequences. Then, a simplified gate recurrent unit network (SGRU) to achieve the prediction of the component with low SE-value. Besides, for the components with high SE-values, the multiple parameters optimization is implemented for RKELM based on slime mould algorithm (SMA), which possesses superior convergence speed and precision. Ultimately, the final prediction results can be obtained by accumulating the predicted values of the multiple models. In the experiment phase, two datasets are adopted to verify the predictive ability of the proposed hybrid EWT-SMA-RKELM-SGRU model, where the sufficient results indicate that the proposed model has a superior performance.

Keywords

• Short-term wind power forecasting
• prediction model allocation
• slime mould algorithm
• multiple parameters optimization
• simplified gate recurrent unit network