A hybrid forecasting system based on fuzzy time series and multi-objective optimization for wind speed forecasting

Ping Jiang, Hufang Yang and Jiani Heng

Applied Energy, 2019, vol. 235, issue C, 786-801

Abstract: Wind speed forecasting is fundamental to the dispatching, controllability, and stability of the power grid. As a challenging but essential work, wind speed forecasting has attracted significant attention from researchers and managers. However, traditional forecasting models sometimes fail to capture data features owing to the randomness and intermittency of wind speed, and the models always focus only on improving accuracy, which is one-sided. Motivated by these problems, in this study, a novel hybrid forecasting system consisting of three modules (a data preprocessing module, optimization module, and forecasting module) is developed to improve the forecasting accuracy and stability. In the data preprocessing module, an effective denoising technique is used to produce a smoother sequence. A fuzzy time series method optimized by a multi-objective differential evolution algorithm that balances the conflict between forecasting accuracy and stability is developed in the next two modules to perform the forecasting. Several experimental results covering different models and wind-speed time intervals all indicate that our proposed hybrid forecasting system can achieve both satisfactory accuracy and stability with a mean absolute percentage error of below 4% in wind speed forecasting. Moreover, the statistical hypothesis test, forecasting effectiveness and grey relational degree are discussed which also demonstrate the great performance of proposed system.

Keywords: Wind speed forecasting; Multi-objective differential evolution; Weighted fuzzy time series; Comprehensive evaluation (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (33)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261918317203
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:235:y:2019:i:c:p:786-801

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2018.11.012

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().