Hybrid model of time series forecasting for possible applications in the wind power sector
DOI:
https://doi.org/10.5902/2179460X30415Keywords:
Statistical model, Artificial neural networks, Wind speedAbstract
In this paper an innovative hybrid model of time series prediction based on the combination of two functions (linear and nonlinear) of the Holt-Winters and Artificial Neural Networks models is presented. This model is applied in wind speed in northeastern Brazil, and was able to perform short and long term forecasts with good accuracy. We highlight the efficiency of the proposed model in providing perfect adjustments to the data observed, being this affirmative according to the low values found in the statistical analysis of errors, for example, with percentage error of approximately 5.0%, and also with the value of the Nash-Sutcliffe coefficient of efficiency of approximately 0.96. These results were important for the accuracy of the data, so that they could follow the profile of the observed time series, mainly revealing greater similarities of maximum and minimum values between both series, thus showing the capacity of the model to represent characteristics of local seasonality. Wind speed prediction methods can be a useful technique in the wind power sector, for example, being able to acquire important information on how local wind potential can be harnessed for possible electric power generation.Downloads
References
BROCKWELL, PJ, DAVIS, RA. Introduction to time series and forecasting. New York: Springer; 2016.
CAMELO, HN, LUCIO, PS, LEAL JUNIOR, JBV. Modelagem de média mensal de velocidade do vento para região litorânea no nordeste Brasileiro através do método aditivo Holt-Winters com vias a previsão de geração eólica. Revista Brasileira de Energias Renováveis 2016;5(4):17-29.
DAI, A. Increasing drought under global warming in observations and models. Nat. Clim. Change 2013;3(1):52-58.
KAVASSERI, R, SEETHARAMAN, K. Day-ahead wind speed forecasting using f-ARIMA models. Renew. Energy 2009; 34(5):1388–1393.
LIU, H, TIAN, H, Li, Y. Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction. Appl. Energy 2012; 98(1):415 – 424.
PACHAURI, RK, MEYER, L, PLATTNER, GK, STOCKER, T. IPCC. Climate Change 2014: Synthesis Report. In: Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC [Internet]; 2015 January 02 - 03; Geneva, Switzerland. 2015 [cited 2017 dec 07]. Available from: http://boris.unibe.ch/71642/.
SILVA, PMO, MELLO, CR, SILVA, AM, COELHO, G. Modelagem da hidrógrafa de cheia em uma bacia hidrográfica da região Alto Rio Grande. Revista Brasileira de Engenharia Agrícola e Ambiental 2008;12(3):258-265.
ZHANG, G. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 2003;50(1):159-175.
Downloads
Published
How to Cite
Issue
Section
License
To access the DECLARATION AND TRANSFER OF COPYRIGHT AUTHOR’S DECLARATION AND COPYRIGHT LICENSE click here.
Ethical Guidelines for Journal Publication
The Ciência e Natura journal is committed to ensuring ethics in publication and quality of articles.
Conformance to standards of ethical behavior is therefore expected of all parties involved: Authors, Editors, Reviewers, and the Publisher.
In particular,
Authors: Authors should present an objective discussion of the significance of research work as well as sufficient detail and references to permit others to replicate the experiments. Fraudulent or knowingly inaccurate statements constitute unethical behavior and are unacceptable. Review Articles should also be objective, comprehensive, and accurate accounts of the state of the art. The Authors should ensure that their work is entirely original works, and if the work and/or words of others have been used, this has been appropriately acknowledged. Plagiarism in all its forms constitutes unethical publishing behavior and is unacceptable. Submitting the same manuscript to more than one journal concurrently constitutes unethical publishing behavior and is unacceptable. Authors should not submit articles describing essentially the same research to more than one journal. The corresponding Author should ensure that there is a full consensus of all Co-authors in approving the final version of the paper and its submission for publication.
Editors: Editors should evaluate manuscripts exclusively on the basis of their academic merit. An Editor must not use unpublished information in the editor's own research without the express written consent of the Author. Editors should take reasonable responsive measures when ethical complaints have been presented concerning a submitted manuscript or published paper.
Reviewers: Any manuscripts received for review must be treated as confidential documents. Privileged information or ideas obtained through peer review must be kept confidential and not used for personal advantage. Reviewers should be conducted objectively, and observations should be formulated clearly with supporting arguments, so that Authors can use them for improving the paper. Any selected Reviewer who feels unqualified to review the research reported in a manuscript or knows that its prompt review will be impossible should notify the Editor and excuse himself from the review process. Reviewers should not consider manuscripts in which they have conflicts of interest resulting from competitive, collaborative, or other relationships or connections with any of the authors, companies, or institutions connected to the papers.