Title | A cascaded deep learning framework for photovoltaic power forecasting with multi-fidelity inputs |
Author | |
Corresponding Author | Zhang,Dongxiao |
Publication Years | 2023-04-01
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DOI | |
Source Title | |
ISSN | 0360-5442
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EISSN | 1873-6785
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Volume | 268 |
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] |
Indexed By | |
Language | English
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SUSTech Authorship | Corresponding
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WOS Research Area | Thermodynamics
; Energy & Fuels
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WOS Subject | Thermodynamics
; Energy & Fuels
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WOS Accession No | WOS:000993981500001
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Publisher | |
ESI Research Field | ENGINEERING
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Scopus EID | 2-s2.0-85146070151
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Data Source | Scopus
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Citation statistics |
Cited Times [WOS]:1
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Document Type | Journal Article |
Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/442604 |
Department | National 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 Affilication | National 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.
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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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