基于机制模型驱动和堆叠模型融合的光伏功率预测

IF 1.6 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Journal of Electrical Engineering & Technology Pub Date : 2024-04-12 DOI:10.1007/s42835-024-01906-8
Fan Chen, Jinjin Ding, Qian Zhang, Junjie Wu, Fan Lei, Yifan Liu
{"title":"基于机制模型驱动和堆叠模型融合的光伏功率预测","authors":"Fan Chen, Jinjin Ding, Qian Zhang, Junjie Wu, Fan Lei, Yifan Liu","doi":"10.1007/s42835-024-01906-8","DOIUrl":null,"url":null,"abstract":"<p>Accurate short-term forecasting of photovoltaic power generation is crucial for power dispatching, capacity analysis, and unit commitment. Existing data-driven prediction algorithms have a certain impact on calculation speed and prediction accuracy, but they fail to consider the internal mechanism of photovoltaic power generation and have the risk of generalization. First, the fuzzy C-means clustering (FCM) algorithm method was used for preprocessing of the PV sample set. The sample points with variability were categorized into different sample sets with less variability. Second, the photovoltaic mechanism model is added to the first layer learner of the Stacking framework to form a one-layer learner of the Long Short-Term Memory (LSTM) neural network, Light Gradient Boosting model (LGBM), and mechanism-driven model. The mechanistic model limits PV generation to a reasonable range as a prediction constraint for the data-driven model. The proposed model can seize the useful inherent information from the mechanism model and utilize the ability of data analysis to extract the inexplicit linear relationship. Finally, the PV power and weather observation data collected from photovoltaic power stations located in a certain place in Germany are used to verify the effectiveness of the proposed method.</p>","PeriodicalId":15577,"journal":{"name":"Journal of Electrical Engineering & Technology","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A PV Power Forecasting Based on Mechanism Model-Driven and Stacking Model Fusion\",\"authors\":\"Fan Chen, Jinjin Ding, Qian Zhang, Junjie Wu, Fan Lei, Yifan Liu\",\"doi\":\"10.1007/s42835-024-01906-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Accurate short-term forecasting of photovoltaic power generation is crucial for power dispatching, capacity analysis, and unit commitment. Existing data-driven prediction algorithms have a certain impact on calculation speed and prediction accuracy, but they fail to consider the internal mechanism of photovoltaic power generation and have the risk of generalization. First, the fuzzy C-means clustering (FCM) algorithm method was used for preprocessing of the PV sample set. The sample points with variability were categorized into different sample sets with less variability. Second, the photovoltaic mechanism model is added to the first layer learner of the Stacking framework to form a one-layer learner of the Long Short-Term Memory (LSTM) neural network, Light Gradient Boosting model (LGBM), and mechanism-driven model. The mechanistic model limits PV generation to a reasonable range as a prediction constraint for the data-driven model. The proposed model can seize the useful inherent information from the mechanism model and utilize the ability of data analysis to extract the inexplicit linear relationship. Finally, the PV power and weather observation data collected from photovoltaic power stations located in a certain place in Germany are used to verify the effectiveness of the proposed method.</p>\",\"PeriodicalId\":15577,\"journal\":{\"name\":\"Journal of Electrical Engineering & Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-04-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Electrical Engineering & Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s42835-024-01906-8\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Electrical Engineering & Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s42835-024-01906-8","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

摘要

准确的光伏发电短期预测对电力调度、容量分析和机组承诺至关重要。现有的数据驱动预测算法对计算速度和预测精度有一定影响,但未能考虑光伏发电的内在机理,存在以偏概全的风险。首先,采用模糊 C-means 聚类(FCM)算法方法对光伏样本集进行预处理。将具有变异性的样本点归类为变异性较小的不同样本集。其次,在堆叠框架的第一层学习器中加入光伏机理模型,形成由长短期记忆(LSTM)神经网络、光梯度提升模型(LGBM)和机理驱动模型组成的单层学习器。机理模型将光伏发电限制在合理范围内,作为数据驱动模型的预测约束。所提出的模型可以从机理模型中获取有用的固有信息,并利用数据分析能力提取不明确的线性关系。最后,利用从德国某地光伏电站收集到的光伏发电量和气象观测数据验证了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A PV Power Forecasting Based on Mechanism Model-Driven and Stacking Model Fusion

Accurate short-term forecasting of photovoltaic power generation is crucial for power dispatching, capacity analysis, and unit commitment. Existing data-driven prediction algorithms have a certain impact on calculation speed and prediction accuracy, but they fail to consider the internal mechanism of photovoltaic power generation and have the risk of generalization. First, the fuzzy C-means clustering (FCM) algorithm method was used for preprocessing of the PV sample set. The sample points with variability were categorized into different sample sets with less variability. Second, the photovoltaic mechanism model is added to the first layer learner of the Stacking framework to form a one-layer learner of the Long Short-Term Memory (LSTM) neural network, Light Gradient Boosting model (LGBM), and mechanism-driven model. The mechanistic model limits PV generation to a reasonable range as a prediction constraint for the data-driven model. The proposed model can seize the useful inherent information from the mechanism model and utilize the ability of data analysis to extract the inexplicit linear relationship. Finally, the PV power and weather observation data collected from photovoltaic power stations located in a certain place in Germany are used to verify the effectiveness of the proposed method.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Electrical Engineering & Technology
Journal of Electrical Engineering & Technology ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
4.00
自引率
15.80%
发文量
321
审稿时长
3.8 months
期刊介绍: ournal of Electrical Engineering and Technology (JEET), which is the official publication of the Korean Institute of Electrical Engineers (KIEE) being published bimonthly, released the first issue in March 2006.The journal is open to submission from scholars and experts in the wide areas of electrical engineering technologies. The scope of the journal includes all issues in the field of Electrical Engineering and Technology. Included are techniques for electrical power engineering, electrical machinery and energy conversion systems, electrophysics and applications, information and controls.
期刊最新文献
Parameter Solution of Fractional Order PID Controller for Home Ventilator Based on Genetic-Ant Colony Algorithm Fault Detection of Flexible DC Grid Based on Empirical Wavelet Transform and WOA-CNN Aggregation and Bidding Strategy of Virtual Power Plant Power Management of Hybrid System Using Coronavirus Herd Immunity Optimizer Algorithm A Review on Power System Security Issues in the High Renewable Energy Penetration Environment
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1