Medium-term wind power forecasting using reduced principal component analysis based random forest model

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Jannet Jamii, Mohamed Trabelsi, Majdi Mansouri, A. Kouadri, Mohamed Faouzi Mimouni, M. Nounou
{"title":"Medium-term wind power forecasting using reduced principal component analysis based random forest model","authors":"Jannet Jamii, Mohamed Trabelsi, Majdi Mansouri, A. Kouadri, Mohamed Faouzi Mimouni, M. Nounou","doi":"10.1177/0309524x231217912","DOIUrl":null,"url":null,"abstract":"Due to its dependence on weather conditions, wind power (WP) forecasting has become a challenge for grid operators. Indeed, the dispatcher needs to predict the WP generation to apply the appropriate energy management strategies. To achieve an accurate WP forecasting, it is important to choose the appropriate input data (weather data). To this end, a medium-term wind power forecasting using reduced principal component analysis (RKPCA) based Random Forest Model is proposed in this paper. Two-stage WP forecasting model is developed. In the first stage, a Kernel Principal Component Analysis (KPCA) and reduced KPCA (RKPCA)-based data pre-processing techniques are applied to select and extract the important input data features (wind speed, wind direction, temperature, pressure, and relative humidity). The main idea behind the RKPCA technique is to use Euclidean distance for reducing the number of observations in the training data set to overcome the problem of computation time and storage costs of the conventional KPCA in the feature extraction phase. In the second stage, a Random Forest (RF) algorithm is proposed to predict the WP for medium-term. To evaluate the performance of the proposed RKPCA-RF technique it has been applied to data extracted from NOAA’S Surface Radiation (SURFRAD) network at Bondville station, located in USA. The presented results show that the proposed RKPCA-RF technique achieved more accurate results than the state-of-the-art methodologies in terms of RMSE (0.09), MAE (0.23), and R2 (0.85). In addition, the proposed technique achieved the lowest overall computation time (CPU).","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":"2 5","pages":""},"PeriodicalIF":16.4000,"publicationDate":"2024-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/0309524x231217912","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

Due to its dependence on weather conditions, wind power (WP) forecasting has become a challenge for grid operators. Indeed, the dispatcher needs to predict the WP generation to apply the appropriate energy management strategies. To achieve an accurate WP forecasting, it is important to choose the appropriate input data (weather data). To this end, a medium-term wind power forecasting using reduced principal component analysis (RKPCA) based Random Forest Model is proposed in this paper. Two-stage WP forecasting model is developed. In the first stage, a Kernel Principal Component Analysis (KPCA) and reduced KPCA (RKPCA)-based data pre-processing techniques are applied to select and extract the important input data features (wind speed, wind direction, temperature, pressure, and relative humidity). The main idea behind the RKPCA technique is to use Euclidean distance for reducing the number of observations in the training data set to overcome the problem of computation time and storage costs of the conventional KPCA in the feature extraction phase. In the second stage, a Random Forest (RF) algorithm is proposed to predict the WP for medium-term. To evaluate the performance of the proposed RKPCA-RF technique it has been applied to data extracted from NOAA’S Surface Radiation (SURFRAD) network at Bondville station, located in USA. The presented results show that the proposed RKPCA-RF technique achieved more accurate results than the state-of-the-art methodologies in terms of RMSE (0.09), MAE (0.23), and R2 (0.85). In addition, the proposed technique achieved the lowest overall computation time (CPU).
使用基于简化主成分分析的随机森林模型进行中期风电预测
由于风力发电(WP)对天气条件的依赖性,风力发电预测已成为电网运营商面临的一项挑战。事实上,调度员需要预测风力发电量,以便采用适当的能源管理策略。要实现准确的风电预测,选择合适的输入数据(天气数据)非常重要。为此,本文提出了一种使用基于还原主成分分析(RKPCA)的随机森林模型进行中期风电预测的方法。该模型分为两个阶段。在第一阶段,应用基于核主成分分析(KPCA)和还原主成分分析(RKPCA)的数据预处理技术来选择和提取重要的输入数据特征(风速、风向、温度、气压和相对湿度)。RKPCA 技术的主要思想是利用欧氏距离减少训练数据集中的观测值数量,以克服传统 KPCA 在特征提取阶段的计算时间和存储成本问题。在第二阶段,提出了一种随机森林(RF)算法来预测中期可湿性粉剂。为了评估所提出的 RKPCA-RF 技术的性能,我们将其应用于从位于美国 Bondville 站的 NOAA 地表辐射(SURFRAD)网络中提取的数据。结果表明,就 RMSE(0.09)、MAE(0.23)和 R2(0.85)而言,所提出的 RKPCA-RF 技术比最先进的方法取得了更精确的结果。此外,该技术的总体计算时间(CPU)也是最低的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信