A New Hybrid Model Based on an Intelligent Optimization Algorithm and a Data Denoising Method to Make Wind Speed Predication

Ping Jiang and Qingli Dong

Mathematical Problems in Engineering, 2015, vol. 2015, 1-16

Abstract:

To mitigate the increase of anxiety resulting from the depletion of fossil fuels and destruction of the ecosystem, wind power, as the most common renewable energy, is a flourishing industry. Thus, accurate wind speed forecasting is critical for the efficient function of wind farms. However, affected by complicated influence factors in meteorology and volatile physical property, wind speed forecasting is difficult and challenging. Based on previous research efforts, an intelligent hybrid model was proposed in this paper in an attempt to tackle this difficult task. First, wavelet transform was utilized to extract the main components of the original wind speed data while eliminating noise. To make better use of the back-propagation artificial neural network, the initial parameters of the network are substituted with optimized ones, which are achieved by using the artificial fish swarm algorithm (AFSA), and the final combination model is employed to conduct wind speed forecasting. A series of data are collected from four different observation sites to test the validity of the proposed model. Through comprehensive comparison with the traditional models, the experiment results clearly indicate that the proposed hybrid model outperforms the traditional single models.

Date: 2015
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2015/714605.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2015/714605.xml (text/xml)

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:hin:jnlmpe:714605

DOI: 10.1155/2015/714605

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().