中文版 | English
Title

A cascaded deep learning framework for photovoltaic power forecasting with multi-fidelity inputs

Author
Corresponding AuthorZhang,Dongxiao
Publication Years
2023-04-01
DOI
Source Title
ISSN
0360-5442
Volume268
Abstract
Accurate forecasts of photovoltaic power (PVP) are essential to the production, transmission, and distribution of electricity in power systems. However, PVP output is strongly weather-dependent, and the forecasting of PVP is highly dependent on the quality of numerical weather prediction (NWP) data. In recent years, a huge volume of numerical weather observation (NWO) data which are strongly associated with PVP output have been collected on-site by widely-installed smart meters and sensors. Appropriately utilizing high-fidelity NWO, in addition to low-fidelity NWP, has great potential in promoting the forecasting capability of deep learning (DL) models. Therefore, this paper proposes a cascaded multi-fidelity deep learning (CMF-DL) framework, which is coordinately driven by the data of both NWO and NWP, to deal with the day-ahead PVP forecasting problem. The proposed CMF-DL framework possesses great compatibility, and thus it can be incorporated with various DL models, such as the long short-term memory (LSTM) model and the gated recurrent unit (GRU) model. Subsequently, incorporated with CMF-DL, two newly-developed forecasting models, i.e., CMF-LSTM and CMF-GRU, are proposed, and datasets from a real-life PV plant are utilized, to evaluate the feasibility and effectiveness of the proposed approaches. From the results, the proposed CMF-LSTM and CMF-GRU show greater forecasting capability and anti-noise ability than the basic LSTM and GRU. Both CMF-LSTM and CMF-GRU can accept noisy NWP data with up to 35% errors. Additionally, compared to the persistence model, the forecasting skills of CMF-LSTM and CMF-GRU can be significantly promoted by 39.87% and 44.02%, respectively. The proposed CMF-LSTM and CMF-GRU also achieve better day-ahead PVP forecasting performance than the widely-used reference models in previous works.
Keywords
URL[Source Record]
Language
English
SUSTech Authorship
Corresponding
ESI Research Field
ENGINEERING
Scopus EID
2-s2.0-85146070151
Data Source
Scopus
Citation statistics
Cited Times [WOS]:0
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/442604
DepartmentNational Center for Applied Mathematics, SUSTech Shenzhen
Affiliation
1.Department of Mathematics and Theories,Peng Cheng Laboratory,Shenzhen,Guangdong,518055,China
2.National Center for Applied Mathematics Shenzhen (NCAMS),Southern University of Science and Technology,Shenzhen,Guangdong,518055,China
Corresponding Author AffilicationNational Center for Applied Mathematics, SUSTech Shenzhen
Recommended Citation
GB/T 7714
Luo,Xing,Zhang,Dongxiao. A cascaded deep learning framework for photovoltaic power forecasting with multi-fidelity inputs[J]. ENERGY,2023,268.
APA
Luo,Xing,&Zhang,Dongxiao.(2023).A cascaded deep learning framework for photovoltaic power forecasting with multi-fidelity inputs.ENERGY,268.
MLA
Luo,Xing,et al."A cascaded deep learning framework for photovoltaic power forecasting with multi-fidelity inputs".ENERGY 268(2023).
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