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Enhancing photovoltaic power forecasting accuracy via AnEn-corrected NWP data and seasonal CNN-LSTM models.

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Title: Enhancing photovoltaic power forecasting accuracy via AnEn-corrected NWP data and seasonal CNN-LSTM models.
Authors: Zhu, Honglu1 (AUTHOR) hongluzhu@ncepu.edu.cn, Gao, Yanan1 (AUTHOR), Liu, Jun2 (AUTHOR), Yuan, Shuai2 (AUTHOR), Li, Sun2 (AUTHOR), Qin, Fang3 (AUTHOR), Li, Dengxuan3 (AUTHOR)
Source: International Journal of Green Energy. 2026, Vol. 23 Issue 9, p1991-2008. 18p.
Subject Terms: *Clean energy investment, Electric power production forecasting, Deep learning, Atmospheric models, Forecasting, Forecasting methodology, Numerical weather forecasting, Machine learning
Abstract: Accurate photovoltaic (PV) power forecasting is critical for grid integration of PV energy. To address large errors in numerical weather prediction (NWP) data and poor seasonal adaptability of traditional models, this study proposes an intelligent forecasting method integrating Analog Ensemble (AnEn)-based NWP correction and a seasonally segmented convolutional neural network – long short-term memory (CNN-LSTM) multi-model framework. First, AnEn calibrates NWP data to improve meteorological input accuracy; specifically, corrected irradiance reduces root mean square error (RMSE) by 14.62% and mean absolute error (MAE) by 21.56%, while corrected temperature decreases RMSE by 11.09% and MAE by 12.67%. Second, seasonal CNN-LSTM models capture distinct PV generation characteristics across seasons. Experimental results show the proposed method outperforms traditional models: overall RMSE is reduced by 5.34% (from 16.58 to 15.69), MAE by 6.78% (from 11.85 to 11.05), and the coefficient of determination (R2) is improved by 0.47% (from 0.958 to 0.962). This work enhances forecasting accuracy and robustness, with strong practical applicability for power system operation [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Green Energy is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: GreenFILE
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Header DbId: 8gh
DbLabel: GreenFILE
An: 194222963
AccessLevel: 6
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PubTypeId: academicJournal
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  Label: Title
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  Data: Enhancing photovoltaic power forecasting accuracy via AnEn-corrected NWP data and seasonal CNN-LSTM models.
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  Data: <searchLink fieldCode="AR" term="%22Zhu%2C+Honglu%22">Zhu, Honglu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hongluzhu@ncepu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Gao%2C+Yanan%22">Gao, Yanan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Jun%22">Liu, Jun</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yuan%2C+Shuai%22">Yuan, Shuai</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Sun%22">Li, Sun</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qin%2C+Fang%22">Qin, Fang</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Dengxuan%22">Li, Dengxuan</searchLink><relatesTo>3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Green+Energy%22">International Journal of Green Energy</searchLink>. 2026, Vol. 23 Issue 9, p1991-2008. 18p.
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  Data: *<searchLink fieldCode="DE" term="%22Clean+energy+investment%22">Clean energy investment</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+power+production+forecasting%22">Electric power production forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Atmospheric+models%22">Atmospheric models</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting+methodology%22">Forecasting methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+weather+forecasting%22">Numerical weather forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink>
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  Label: Abstract
  Group: Ab
  Data: Accurate photovoltaic (PV) power forecasting is critical for grid integration of PV energy. To address large errors in numerical weather prediction (NWP) data and poor seasonal adaptability of traditional models, this study proposes an intelligent forecasting method integrating Analog Ensemble (AnEn)-based NWP correction and a seasonally segmented convolutional neural network – long short-term memory (CNN-LSTM) multi-model framework. First, AnEn calibrates NWP data to improve meteorological input accuracy; specifically, corrected irradiance reduces root mean square error (RMSE) by 14.62% and mean absolute error (MAE) by 21.56%, while corrected temperature decreases RMSE by 11.09% and MAE by 12.67%. Second, seasonal CNN-LSTM models capture distinct PV generation characteristics across seasons. Experimental results show the proposed method outperforms traditional models: overall RMSE is reduced by 5.34% (from 16.58 to 15.69), MAE by 6.78% (from 11.85 to 11.05), and the coefficient of determination (R2) is improved by 0.47% (from 0.958 to 0.962). This work enhances forecasting accuracy and robustness, with strong practical applicability for power system operation [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Green Energy is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/15435075.2026.2642171
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        Text: English
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        StartPage: 1991
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      – SubjectFull: Clean energy investment
        Type: general
      – SubjectFull: Electric power production forecasting
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      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Atmospheric models
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      – TitleFull: Enhancing photovoltaic power forecasting accuracy via AnEn-corrected NWP data and seasonal CNN-LSTM models.
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            NameFull: Zhu, Honglu
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            – D: 01
              M: 07
              Text: 2026
              Type: published
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            – TitleFull: International Journal of Green Energy
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