Forecasting model for wind power integrating least squares support vector machine, singular spectrum analysis, deep belief network, and locality‐sensitive hashing-Reference-Cited by-同舟云学术

Forecasting model for wind power integrating least squares support vector machine, singular spectrum analysis, deep belief network, and locality‐sensitive hashing

Published:2020-02 Issue:2 Volume:23 Page:235-257
ISSN:1095-4244
Container-title:Wind Energy
language:en
Short-container-title:Wind Energy

Author:

Habib Arslan¹^ORCID,Abbassi Rabeh²³⁴,Aristizábal Andrés Julián⁵,Abbassi Abdelkader⁴

Affiliation:

1. Faculty of Engineering, Department of Electronic and Electrical EngineeringUniversity of Strathclyde Glasgow UK

2. Department of Electrical Engineering, College of EngineeringUniversity of Hail Hail City Saudi Arabia

3. LaTICE Laboratory, Higher National Engineering School of Tunis (ENSIT)University of Tunis Tunis Tunisia

4. Institute of Applied Sciences and Technology of Kasserine (ISSATKas)University of Kairouan Kasserine Tunisia

5. Engineering DepartmentUniversidad de Bogota Jorge Tadeo Lozano Bogota Colombia

Publisher

Wiley

Subject

Renewable Energy, Sustainability and the Environment

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/we.2425

Reference47 articles.

1. AWNN-Assisted Wind Power Forecasting Using Feed-Forward Neural Network

2. Combined forecasting models for wind energy forecasting: A case study in China

3. Wind wake influence estimation on energy production of wind farm by adaptive neuro-fuzzy methodology

4. Distributed Renewable Energy under the Guidance of Price Autonomous Operation Technology

5. Current status and future advances for wind speed and power forecasting

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