Short-Term Global Horizontal Irradiance Forecasting Using a Hybrid Convolutional Neural Network-Gate Recurrent Unit Method Article Swipe
Yu Zhang
,
Jianfeng Ma
,
Cong Zeng
,
Guangyao Li
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2025/1/012001
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2025/1/012001
The effectiveness of photovoltaic power generation systems—a clean and renewable use of solar energy—depends on the amount of solar radiation. Consequently, solar radiation forecasting (especially short-term) is crucial for photovoltaic plants. In this paper, a hybrid convolutional neural network (CNN) and gate recurrent unit (GRU) method is proposed for short-term (10-min) solar radiation forecasting based on image and time-series data (i.e., radiation data at different times). The method aims to achieve high performance in solar radiation forecasting, which can be useful for PV plant adjustment. CNN–GRU consists of two branches. One is based on the ResNet-18 structure, which can extract features from sky images. The other is a GRU branch, which consists of three fully connected layers used for meteorological feature extraction. Experiments on a public dataset showed that our method predicts the mean absolute error better than other benchmark models. The ablation experiments demonstrated that the hybrid model performs better than a single model and, therefore, shows promise for application in solar radiation forecasting.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/2025/1/012001
- https://iopscience.iop.org/article/10.1088/1742-6596/2025/1/012001/pdf
- OA Status
- diamond
- Cited By
- 7
- References
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3204539100
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- OpenAlex ID
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https://openalex.org/W3204539100Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1742-6596/2025/1/012001Digital Object Identifier
- Title
-
Short-Term Global Horizontal Irradiance Forecasting Using a Hybrid Convolutional Neural Network-Gate Recurrent Unit MethodWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-09-01Full publication date if available
- Authors
-
Yu Zhang, Jianfeng Ma, Cong Zeng, Guangyao LiList of authors in order
- Landing page
-
https://doi.org/10.1088/1742-6596/2025/1/012001Publisher landing page
- PDF URL
-
https://iopscience.iop.org/article/10.1088/1742-6596/2025/1/012001/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://iopscience.iop.org/article/10.1088/1742-6596/2025/1/012001/pdfDirect OA link when available
- Concepts
-
Computer science, Photovoltaic system, Convolutional neural network, Benchmark (surveying), Artificial neural network, Renewable energy, Irradiance, Term (time), Radiation, Artificial intelligence, Engineering, Optics, Electrical engineering, Physics, Cartography, Quantum mechanics, GeographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 2, 2022: 1Per-year citation counts (last 5 years)
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21Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2095466882, https://openalex.org/W2061482009, https://openalex.org/W2034949274, https://openalex.org/W6688647373, https://openalex.org/W6735460956, https://openalex.org/W2901076696, https://openalex.org/W2553152989, https://openalex.org/W2569349941, https://openalex.org/W2792921542, https://openalex.org/W3021445785, https://openalex.org/W6776986427, https://openalex.org/W6687483927, https://openalex.org/W3037712939, https://openalex.org/W3095329050, https://openalex.org/W2963156400, https://openalex.org/W1989649856, https://openalex.org/W2218681948, https://openalex.org/W2194775991, https://openalex.org/W2599971942, https://openalex.org/W2589910628, https://openalex.org/W3022426791 |
| referenced_works_count | 21 |
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| citation_normalized_percentile.is_in_top_10_percent | False |