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Day-Ahead Hourly Forecasting of Solar Generation Based on Cluster Analysis and Ensemble Model | |
Pan, Cheng1,2; Tan, Jie1 | |
发表期刊 | IEEE ACCESS |
ISSN | 2169-3536 |
2019 | |
卷号 | 7期号:7页码:112921-112930 |
摘要 | Accurate solar generation prediction is of great significance for grid dispatching and operation of photovoltaic power plants. In this paper, we propose a novel solar generation forecasting method based on cluster analysis and ensemble model. Two common ways to improve prediction accuracy are adopted. We first conduct cluster analysis based on solar generation to obtain a weather regime, which improves the computational efficiency and avoids the difficulty in selecting weather variables to participate in the clustering process. Then random forests with different parameters is established for different weather regimes, which is used as component models in the followed ensemble model. Finally, we weighted the predictions from different weather regimes to get the final results. To avoid manual design weights, ridge regression is used to calculate weights for each weather regime automatically. A large number of experiments have been carried out on freely available data sets to verify the performance of the proposed method. The experimental results show that our method predicts solar generation more accurately, which has broad prospects in practical application. |
关键词 | Cluster analysis ensemble model ridge regression solar generation forecasting |
DOI | 10.1109/ACCESS.2019.2935273 |
关键词[WOS] | NEURAL-NETWORK ; POWER ; TERM ; PREDICTION |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[U1801263] ; National Natural Science Foundation of China[U1801263] |
WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
WOS类目 | Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications |
WOS记录号 | WOS:000484307300004 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 计算智能 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/27254 |
专题 | 中科院工业视觉智能装备工程实验室_工业智能技术与系统 |
通讯作者 | Tan, Jie |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Pan, Cheng,Tan, Jie. Day-Ahead Hourly Forecasting of Solar Generation Based on Cluster Analysis and Ensemble Model[J]. IEEE ACCESS,2019,7(7):112921-112930. |
APA | Pan, Cheng,&Tan, Jie.(2019).Day-Ahead Hourly Forecasting of Solar Generation Based on Cluster Analysis and Ensemble Model.IEEE ACCESS,7(7),112921-112930. |
MLA | Pan, Cheng,et al."Day-Ahead Hourly Forecasting of Solar Generation Based on Cluster Analysis and Ensemble Model".IEEE ACCESS 7.7(2019):112921-112930. |
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