Artificial Intelligent Power Forecasting for Wind Farm Based on Multi-Source Data Fusion

Author:

Wang Qingtian1,Wang Yunjing1,Zhang Kegong2,Liu Yaxin1,Qiang Weiwei2,Han Wen Qiuzi1

Affiliation:

1. China Huaneng Clean Energy Research Institute, Beijing 102209, China

2. Huaneng Jiuquan Wind Power Co., Ltd., Jiuquan 735000, China

Abstract

Wind power forecasting is a typical high-dimensional and multi-step time series prediction problem. Data-driven prediction methods using machine learning show advantages over traditional physical or statistical methods, especially for wind farms with complex meteorological conditions. Thus, effective use of different data sources and data types will help improve power forecasting accuracy. In this paper, a multi-source data fusion method is proposed, which integrates the static information of the wind turbine with observational and forecasting meteorological information together to further improve the power forecasting accuracy. Firstly, the characteristics of each time step are re-characterized by using the self-attention mechanism to integrate the global information of multi-source data, and the Res-CNN network is used to fuse multi-source data to improve the prediction ability of input variables. Secondly, static variable encoding and feature selection are carried out, and the time-varying variables are combined with static variables for collaborative feature selection, so as to effectively eliminate redundant information. A forecasting model based on the Encoder–Decoder framework is constructed with LSTM as the basic unit, and the Add&Norm mechanism is introduced to further enhance the input variable information. In addition, the self-attention mechanism is used to integrate the global time information of the decoded results, and the Time Distributed mechanism is used to carry out multi-step prediction. Our training and testing data are obtained from an operating wind farm in northwestern China. Results show that the proposed method outperforms a classic AI forecasting method such as that using the Seq2Seq+attention model in terms of prediction accuracy, thus providing an effective solution for multi-step forecasting of wind power in wind farms.

Funder

China Huaneng Clean Energy Research Institute

Publisher

MDPI AG

Subject

Process Chemistry and Technology,Chemical Engineering (miscellaneous),Bioengineering

Reference33 articles.

1. Optimal Power Spinning Reserve Method of Concentrating Solar Power and Thermal Power for High-Proportion Wind Power System;Zhang;Trans. China Electrotech. Soc.,2022

2. A comprehensive review on deep learning approaches in wind forecasting applications;Wu;CAAI Trans. Intell. Technol.,2022

3. Review on Key Technologies and Applications in Wind Power Forecasting;Sun;High Volt. Eng.,2021

4. Methodology for Calculating VRE Equivalent Feed-In Tariff Based on System Cost and Its Application;Zhang;Electr. Power,2022

5. A heuristic methodology to economic dispatch problem incorporating renewable power forecasting error and system reliability;Osorio;Renew. Energy,2016

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