A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting

•A combined model based on advanced optimization algorithm is successfully proposed.•Design three experiments from the real wind farm to verify the effectiveness.•The proposed combined model can enhance the forecasting accuracy significantly.•Experiments demonstrate the availability and reliability...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Applied energy Jg. 215; S. 643 - 658
Hauptverfasser: Song, Jingjing, Wang, Jianzhou, Lu, Haiyan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.04.2018
Schlagworte:
Advanced optimization algorithm
algorithms
case studies
China
Combined model
Data preprocessing technique
electrical equipment
financial economics
planning
power generation
wind farms
wind power
wind speed
Wind speed forecasting
China
Combined model
Advanced optimization algorithm
Data preprocessing technique
Wind speed forecasting
ISSN:0306-2619, 1872-9118
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:•A combined model based on advanced optimization algorithm is successfully proposed.•Design three experiments from the real wind farm to verify the effectiveness.•The proposed combined model can enhance the forecasting accuracy significantly.•Experiments demonstrate the availability and reliability of the developed model. Short-term wind speed forecasting has a significant influence on enhancing the operation efficiency and increasing the economic benefits of wind power generation systems. A substantial number of wind speed forecasting models, which are aimed at improving the forecasting performance, have been proposed. However, some conventional forecasting models do not consider the necessity and importance of data preprocessing. Moreover, they neglect the limitations of individual forecasting models, leading to poor forecasting accuracy. In this study, a novel model combining a data preprocessing technique, forecasting algorithms, an advanced optimization algorithm, and no negative constraint theory is developed. This combined model successfully overcomes some limitations of the individual forecasting models and effectively improves the forecasting accuracy. To estimate the effectiveness of the proposed combined model, 10-min wind speed data from the wind farm in Peng Lai, China are used as case studies. The experiment results demonstrate that the developed combined model is definitely superior compared to all other conventional models. Furthermore, it can be used as an effective technique for smart grid planning.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2018.02.070