Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/14180
Title: A data-driven short-term forecasting model for offshore wind speed prediction based on computational intelligence
Authors: Panapakidis, Ioannis P. 
Michailides, Constantine 
Angelides, Demos C. 
Keywords: Computational intelligence;Offshore wind;Forecasting;Machine learning;Neural networks;Neuro-fuzzy systems
Category: Electrical Engineering - Electronic Engineering - Information Engineering
Field: Engineering and Technology
Issue Date: Apr-2019
Source: Electronics, 2019, Volume 8, Issue 4, Article Number 420
Journal: Electronics 
Abstract: Wind speed forecasting is an important element for the further development of offshore wind turbines. Due to its importance, many researchers have proposed different models for wind speed forecasting that differ in terms of the time-horizon of the forecast, types and number of inputs, complexity, structure, and others. Wind speed series present high nonlinearity and volatilities, and thus an effective model should successfully deal with those features. An approach to deal with the nonlinearities and volatilities is to utilize a time series processing technique such as the wavelet transform. In the present paper, an ensemble data-driven short-term wind speed forecasting model is developed, tested and applied. The term “ensemble” refers to the combination of two different predictors that run in parallel and the prediction is obtained by the predictor that leads to the lowest error. The proposed model utilizes the wavelet transform and is compared with other models that have been presented in the related literature and outperforms their accuracy. The proposed forecasting model can be used effectively for 1 min and 10 min ahead horizon wind speed predictions.
URI: https://ktisis.cut.ac.cy/handle/10488/14180
ISSN: 2079-9292
DOI: 10.3390/electronics8040420
Rights: © 2019 by the authors. Licensee MDPI, Basel, Switzerland.
Type: Article
Appears in Collections:Άρθρα/Articles

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