A Novel Wind Power Forecasting Based Feature Selection and Hybrid Forecast Engine Bundled with Honey Bee Mating Optimization

Mehdi Bagheri, Venera Nurmanova, Oveis Abedinia, Mohammad Salay Naderi, Mehdi Salay Naderi, Noradin Ghadimi

2018

Closed access Metadata (CSL-JSON)
DOI 10.1109/eeeic.2018.8493805
Data confidence 85 high
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Abstract

In this study, new forecasting approach is developed for wind power signal based on Empirical Mode Decomposition (EMD), feature selection and forecast engine. Due to high volatility of wind power signal, we used a complex prediction approach with hybrid forecast engine. The proposed forecast engine is coupled with an intelligent algorithm to improve the training mechanism and optimize free parameters. To show the abilities of proposed forecasting approach real test case is considered by comparison with other strategies. In order to proof the superiority of suggested method, it is compared with various prediction approaches. Generated results confirm the validity of this strategy.