Multiperiod-Ahead Wind Speed Forecasting Using Deep Neural Architecture and Ensemble Learning
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-06-27
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Accurate forecasting of wind speed plays a fundamental role in enabling reliable operation and planning for large-scale integration of wind turbines.
It is difficult to obtain the accurate wind speed forecasting (WSF) due to the intermittent and random nature of wind energy.
In this paper, a multiperiod-ahead WSF model based on the analysis of variance, stacked denoising autoencoder (SDAE), and ensemble learning is proposed.
The analysis of variance classifies the training samples into different categories.
The stacked denoising autoencoder as a deep learning architecture is later built for unsupervised feature learning in each category.
The ensemble of extreme learning machine (ELM) is applied to fine-tune the SDAE for multiperiod-ahead wind speed forecasting.
Experimental results are made to demonstrate that the proposed model has the best performance compared with the classic WSF methods including the single SDAE-ELM, ELMAN, and adaptive neuron-fuzzy inference system (ANFIS).
American Psychological Association (APA)
Chen, Lei& Li, Zhijun& Zhang, Yi. 2019. Multiperiod-Ahead Wind Speed Forecasting Using Deep Neural Architecture and Ensemble Learning. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1198047
Modern Language Association (MLA)
Chen, Lei…[et al.]. Multiperiod-Ahead Wind Speed Forecasting Using Deep Neural Architecture and Ensemble Learning. Mathematical Problems in Engineering No. 2019 (2019), pp.1-14.
https://search.emarefa.net/detail/BIM-1198047
American Medical Association (AMA)
Chen, Lei& Li, Zhijun& Zhang, Yi. Multiperiod-Ahead Wind Speed Forecasting Using Deep Neural Architecture and Ensemble Learning. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1198047
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1198047