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Cloud Map Multi-Feature Extraction for Ultra-Short-Term Photovoltaic Power Forecasting.

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Title: Cloud Map Multi-Feature Extraction for Ultra-Short-Term Photovoltaic Power Forecasting.
Authors: Zhang, Na1,2 (AUTHOR) zhangna2010337@163.com, Guo, Dianting1,2 (AUTHOR), Zhao, Qianyu3 (AUTHOR), Li, Ruifan4 (AUTHOR), Guo, Jing1 (AUTHOR)
Source: Energies (19961073). Sep2026, Vol. 19 Issue 17, p3978. 23p.
Subject Terms: *Forecasting, *Image processing, *Optical flow, *Energy industry forecasting, *Convolutional neural networks, *Deep learning, *Multisensor data fusion
Abstract: To address the reduced accuracy of photovoltaic power forecasting caused by the fluctuating characteristics of ultra-short-term photovoltaic output, this paper proposes an ultra-short-term photovoltaic power forecasting method that integrates static and dynamic features extracted from ground-based cloud images with deep learning. The proposed method employs distortion correction, histogram equalization, and other techniques for image quality enhancement. Static cloud-image features are extracted using a threshold segmentation algorithm based on the maximum inter-class variance method. To characterize the dynamic evolution of cloud clusters, an optical flow method is introduced to accurately capture their motion speed and direction. The extracted static and dynamic cloud features are then combined to form a fused dataset. Finally, an ultra-short-term forecasting model combining a convolutional neural network and Autoformer is developed. Comparative results under different weather conditions show that the incorporation of multiple cloud-image features significantly improves forecasting accuracy, thereby validating the effectiveness of multimodal data fusion and the proposed deep learning architecture. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 197040459
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
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  Data: Cloud Map Multi-Feature Extraction for Ultra-Short-Term Photovoltaic Power Forecasting.
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  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Na%22">Zhang, Na</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> zhangna2010337@163.com</i><br /><searchLink fieldCode="AR" term="%22Guo%2C+Dianting%22">Guo, Dianting</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Qianyu%22">Zhao, Qianyu</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Ruifan%22">Li, Ruifan</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Guo%2C+Jing%22">Guo, Jing</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Sep2026, Vol. 19 Issue 17, p3978. 23p.
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  Data: *<searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br />*<searchLink fieldCode="DE" term="%22Optical+flow%22">Optical flow</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+industry+forecasting%22">Energy industry forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: To address the reduced accuracy of photovoltaic power forecasting caused by the fluctuating characteristics of ultra-short-term photovoltaic output, this paper proposes an ultra-short-term photovoltaic power forecasting method that integrates static and dynamic features extracted from ground-based cloud images with deep learning. The proposed method employs distortion correction, histogram equalization, and other techniques for image quality enhancement. Static cloud-image features are extracted using a threshold segmentation algorithm based on the maximum inter-class variance method. To characterize the dynamic evolution of cloud clusters, an optical flow method is introduced to accurately capture their motion speed and direction. The extracted static and dynamic cloud features are then combined to form a fused dataset. Finally, an ultra-short-term forecasting model combining a convolutional neural network and Autoformer is developed. Comparative results under different weather conditions show that the incorporation of multiple cloud-image features significantly improves forecasting accuracy, thereby validating the effectiveness of multimodal data fusion and the proposed deep learning architecture. [ABSTRACT FROM AUTHOR]
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        Value: 10.3390/en19173978
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        Text: English
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        Type: general
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      – SubjectFull: Optical flow
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      – SubjectFull: Energy industry forecasting
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      – SubjectFull: Convolutional neural networks
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      – SubjectFull: Deep learning
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      – SubjectFull: Multisensor data fusion
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            – D: 01
              M: 09
              Text: Sep2026
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              Y: 2026
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