An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks

Author:

Niksa-Rynkiewicz Tacjana1ORCID,Stomma Piotr2ORCID,Witkowska Anna3ORCID,Rutkowska Danuta4ORCID,Słowik Adam5ORCID,Cpałka Krzysztof6ORCID,Jaworek-Korjakowska Joanna7ORCID,Kolendo Piotr8ORCID

Affiliation:

1. 1 Gdańsk University of Technology , Faculty of Ocean Engineering and Ship Technology , Gdańsk , Poland

2. 2 University of Białystok , Institute of Computer Science , Białystok , Poland

3. 3 Gdańsk University of Technology , Faculty of Electrical and Control Engineering , Gdańsk , Poland

4. 4 University of Social Sciences , Information Technology Institute , Łódź , Poland

5. 5 Koszalin University of Technology , Department of Electronics and Computer Science , Koszalin , Poland

6. 6 Częstochowa University of Technology , Department of Intelligent Computer Systems , Częstochowa , Poland

7. 7 AGH University , Department of Automatic Control and Robotics, Center of Excellence in Artificial Intelligence Kraków , Poland

8. 8 Institute of Power Engineering , Department of Power Automation , Gdańsk , Poland

Abstract

Abstract In this paper, an intelligent approach to the Short-Term Wind Power Prediction (STWPP) problem is considered, with the use of various types of Deep Neural Networks (DNNs). The impact of the prediction time horizon length on accuracy, and the influence of temperature on prediction effectiveness have been analyzed. Three types of DNNs have been implemented and tested, including: CNN (Convolutional Neural Networks), GRU (Gated Recurrent Unit), and H-MLP (Hierarchical Multilayer Perceptron). The DNN architectures are part of the Deep Learning Prediction (DLP) framework that is applied in the Deep Learning Power Prediction System (DLPPS). The system is trained based on data that comes from a real wind farm. This is significant because the prediction results strongly depend on weather conditions in specific locations. The results obtained from the proposed system, for the real data, are presented and compared. The best result has been achieved for the GRU network. The key advantage of the system is a high effectiveness prediction using a minimal subset of parameters. The prediction of wind power in wind farms is very important as wind power capacity has shown a rapid increase, and has become a promising source of renewable energies.

Publisher

Walter de Gruyter GmbH

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Hardware and Architecture,Modeling and Simulation,Information Systems

Reference55 articles.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3