Research on a Novel Combination System on the Basis of Deep Learning and Swarm Intelligence Optimization Algorithm for Wind Speed Forecasting

IEEE Access

Xiaohui He¹  Hengliang Guo¹  Jianzhou Wang²  Ying Nie² 

[1] School of Geoscience and Technology, Zhengzhou University, Zhengzhou, China;School of Statistics, Dongbei University of Finance and Economics, Dalian, China;

关键词: Wind speed forecasting;    deep learning;    multi-objective optimization algorithm;    combination system;   

DOI  :  10.1109/ACCESS.2020.2980562

来源: DOAJ

【 摘 要 】

Wind speed forecasting takes a significant place in electric system owing to the fact that it has significant influence on operation efficiency and economic benefits. Aimming at improving forecast performance, a substantial number of wind speed prediction models have been proposed. However, these models have disregarded the limits of individual prediction models and the necessity of data preprocessing, resulting in poor prediction accuracy. In this study, a novel forecasting system is proposed consisting of three modules: data preprocessing module, individual forecasting module and weight optimization module, which effectively achieve better forecasting ability. For data preprocessing and individual forecasting module, more regular sequences are obtained by decomposition technology, and association features are extracted by deep learning algorithm in prediction module. In the weight optimized module, the combination method base on the multi-objective optimization algorithm and nonnegative constraint theory are used to improve the prediction effectiveness. The combination model successfully exceeds the limits of individual predicton models and comparatively improves prediction accuracy. The effectiveness of the developed combination system is evaluated by 10-min wind speed in Penglai, China. The experiment results indicate that proposed forecasting system is better than other traditional forecasting models on three real wind speed datasets indeed.

【 授权许可】

Unknown