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Enhanced PV power prediction using LSTM-integrated soft actor–critic model based on long short-term memory.

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Title: Enhanced PV power prediction using LSTM-integrated soft actor–critic model based on long short-term memory.
Authors: Xu, Yang1 (AUTHOR) xuyang688882@163.com, Lu, Zhengqiu1 (AUTHOR)
Source: Journal of Computational Methods in Sciences & Engineering (Sage Publications Inc.). Sep2025, Vol. 25 Issue 5, p4774-4786. 13p.
Subjects: Reinforcement learning, Deep reinforcement learning, Long short-term memory, Markov processes, Electric power distribution grids, Deep learning
Abstract: Accurate PV power prediction is crucial in efficiently operating intelligent power grid systems. Data-driven approaches have shown high performance in predictive tasks. Deep reinforcement learning (DRL) merges deep learning with reinforcement learning and has been widely studied for optimization challenges in various fields. However, limited research has focused on applying DRL to ultra-short-term PV power prediction. Hence, a soft actor–critic (SAC) model using long short-term memory (LSTM) is proposed for predicting PV power. To accomplish this, first, the PV power problem is modeled as a Markov decision process with historical weather data and PV power data as state inputs. Then, LSTM is integrated into the critic network of SAC to enhance its memory capability, thus improving prediction accuracy. Ultimately, the agent engages with the environment to address the optimization problem. Experimental results indicate that the proposed model attains greater prediction accuracy. This study explores the potential of DRL for PV power prediction, and the proposed method can be extended to other prediction fields, including grid prediction and wind power prediction. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Computational Methods in Sciences & Engineering (Sage Publications Inc.) is the property of Sage Publications Inc. 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.)
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  Data: Enhanced PV power prediction using LSTM-integrated soft actor–critic model based on long short-term memory.
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  Data: <searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+reinforcement+learning%22">Deep reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+processes%22">Markov processes</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+power+distribution+grids%22">Electric power distribution grids</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Accurate PV power prediction is crucial in efficiently operating intelligent power grid systems. Data-driven approaches have shown high performance in predictive tasks. Deep reinforcement learning (DRL) merges deep learning with reinforcement learning and has been widely studied for optimization challenges in various fields. However, limited research has focused on applying DRL to ultra-short-term PV power prediction. Hence, a soft actor–critic (SAC) model using long short-term memory (LSTM) is proposed for predicting PV power. To accomplish this, first, the PV power problem is modeled as a Markov decision process with historical weather data and PV power data as state inputs. Then, LSTM is integrated into the critic network of SAC to enhance its memory capability, thus improving prediction accuracy. Ultimately, the agent engages with the environment to address the optimization problem. Experimental results indicate that the proposed model attains greater prediction accuracy. This study explores the potential of DRL for PV power prediction, and the proposed method can be extended to other prediction fields, including grid prediction and wind power prediction. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Computational Methods in Sciences & Engineering (Sage Publications Inc.) is the property of Sage Publications Inc. 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: 10.1177/14727978251337946
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      – Code: eng
        Text: English
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        PageCount: 13
        StartPage: 4774
    Subjects:
      – SubjectFull: Reinforcement learning
        Type: general
      – SubjectFull: Deep reinforcement learning
        Type: general
      – SubjectFull: Long short-term memory
        Type: general
      – SubjectFull: Markov processes
        Type: general
      – SubjectFull: Electric power distribution grids
        Type: general
      – SubjectFull: Deep learning
        Type: general
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      – TitleFull: Enhanced PV power prediction using LSTM-integrated soft actor–critic model based on long short-term memory.
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            NameFull: Lu, Zhengqiu
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            – D: 01
              M: 09
              Text: Sep2025
              Type: published
              Y: 2025
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