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Probabilistic Forecasting of Regional Photovoltaic Power Based on QR-STGAT.

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Title: Probabilistic Forecasting of Regional Photovoltaic Power Based on QR-STGAT.
Authors: Tang, Xuchen1 (AUTHOR), Chen, Huican1,2 (AUTHOR), Lu, Qiqi2,3 (AUTHOR) 202421017195@mail.scut.edu.cn, Fu, Cong1 (AUTHOR), Zeng, Jingyao2,3 (AUTHOR), Yang, Yun1,3 (AUTHOR), Zeng, Jun3 (AUTHOR)
Source: Energies (19961073). Jul2026, Vol. 19 Issue 13, p3108. 19p.
Subject Terms: *Photovoltaic power generation, *Spatiotemporal processes, *Graph neural networks, *Forecasting, *Electric power system stability, *Energy industry forecasting, *Quantile regression
Geographic Terms: Guangdong Sheng (China)
Abstract: As the penetration rate of photovoltaic power generation continues to increase within new power systems, accurately forecasting regional PV power output has become critical to ensuring the safe and stable operation of power grids. Photovoltaic power generation exhibits significant spatio-temporal correlations, and traditional single-site forecasting methods struggle to fully capture the spatial dependencies among multiple PV plants within a region. To address this challenge, this study proposes a unified QR-STGAT probabilistic forecasting framework that jointly captures adaptive spatial dependencies via graph attention mechanisms and multi-scale temporal dynamics via a CNN-GRU architecture, while enabling end-to-end uncertainty quantification through integrated quantile regression. The framework is validated on 15 min resolution PV output data collected from five prefecture-level cities in Guangdong Province over a seven-month period from January to July 2025, and compared against baselines including BiLSTM and Transformer. Experimental results demonstrate that the proposed method reduces RMSE by up to 11.61% over baseline models and achieves a PICP of 93.05% at the 95% confidence level, providing a more reliable reference for power system dispatch decisions. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 195441260
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
  Group: Ti
  Data: Probabilistic Forecasting of Regional Photovoltaic Power Based on QR-STGAT.
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  Data: <searchLink fieldCode="AR" term="%22Tang%2C+Xuchen%22">Tang, Xuchen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Huican%22">Chen, Huican</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lu%2C+Qiqi%22">Lu, Qiqi</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> 202421017195@mail.scut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Fu%2C+Cong%22">Fu, Cong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zeng%2C+Jingyao%22">Zeng, Jingyao</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Yun%22">Yang, Yun</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zeng%2C+Jun%22">Zeng, Jun</searchLink><relatesTo>3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jul2026, Vol. 19 Issue 13, p3108. 19p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Photovoltaic+power+generation%22">Photovoltaic power generation</searchLink><br />*<searchLink fieldCode="DE" term="%22Spatiotemporal+processes%22">Spatiotemporal processes</searchLink><br />*<searchLink fieldCode="DE" term="%22Graph+neural+networks%22">Graph neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+power+system+stability%22">Electric power system stability</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+industry+forecasting%22">Energy industry forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Quantile+regression%22">Quantile regression</searchLink>
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  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Guangdong+Sheng+%28China%29%22">Guangdong Sheng (China)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: As the penetration rate of photovoltaic power generation continues to increase within new power systems, accurately forecasting regional PV power output has become critical to ensuring the safe and stable operation of power grids. Photovoltaic power generation exhibits significant spatio-temporal correlations, and traditional single-site forecasting methods struggle to fully capture the spatial dependencies among multiple PV plants within a region. To address this challenge, this study proposes a unified QR-STGAT probabilistic forecasting framework that jointly captures adaptive spatial dependencies via graph attention mechanisms and multi-scale temporal dynamics via a CNN-GRU architecture, while enabling end-to-end uncertainty quantification through integrated quantile regression. The framework is validated on 15 min resolution PV output data collected from five prefecture-level cities in Guangdong Province over a seven-month period from January to July 2025, and compared against baselines including BiLSTM and Transformer. Experimental results demonstrate that the proposed method reduces RMSE by up to 11.61% over baseline models and achieves a PICP of 93.05% at the 95% confidence level, providing a more reliable reference for power system dispatch decisions. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.3390/en19133108
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 19
        StartPage: 3108
    Subjects:
      – SubjectFull: Photovoltaic power generation
        Type: general
      – SubjectFull: Spatiotemporal processes
        Type: general
      – SubjectFull: Graph neural networks
        Type: general
      – SubjectFull: Forecasting
        Type: general
      – SubjectFull: Electric power system stability
        Type: general
      – SubjectFull: Energy industry forecasting
        Type: general
      – SubjectFull: Quantile regression
        Type: general
      – SubjectFull: Guangdong Sheng (China)
        Type: general
    Titles:
      – TitleFull: Probabilistic Forecasting of Regional Photovoltaic Power Based on QR-STGAT.
        Type: main
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          Name:
            NameFull: Tang, Xuchen
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            NameFull: Chen, Huican
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            NameFull: Lu, Qiqi
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            NameFull: Fu, Cong
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            NameFull: Zeng, Jingyao
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            NameFull: Yang, Yun
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            – D: 01
              M: 07
              Text: Jul2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19961073
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              Value: 19
            – Type: issue
              Value: 13
          Titles:
            – TitleFull: Energies (19961073)
              Type: main
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