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Stacked LSTM for Short-Term Wind Power Forecasting Using Multivariate Time Series Data.

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Titel: Stacked LSTM for Short-Term Wind Power Forecasting Using Multivariate Time Series Data.
Autoren: Galphade, Manisha, Nikam, V. B., Banerjee, Biplab, Kiwelekar, Arvind W., Sharma, Priyanka
Quelle: International Journal of Interactive Multimedia & Artificial Intelligence; Jun2025, Vol. 9 Issue 3, p71-81, 11p
Schlagwörter: LONG short-term memory, WIND forecasting, POWER resources, DEEP learning, RENEWABLE energy sources
Abstract: Currently, wind power is the fast growing area in the domain of renewable energy generation. Accurate prediction of wind power output in wind farms is crucial for addressing the challenges associated the power grid. This precise forecasting enables grid operators to enhance safety and optimize grid operations by effectively managing fluctuations in power generation, ensuring a reliable and stable energy supply. In recent years, there has been a significant rise in research and investigations conducted in this field. This study aims to develop a multivariate short-term wind power forecasting (WPF) model with the objective of enhancing forecasting precision. Among the various prediction models, deep learning models such as Long Short-Term Memory (LSTM) have demonstrated outstanding performance in the field of WPF. By adding multiple layers of LSTM networks, the model can capture more complex patterns. To improve the performance, data preprocessing is carried out using two techniques such as removal of missing values and imputing missing values using Random Forest Regressor (RFR). The comparison between the proposed Stacked LSTM model and other methods including vector autoregressive (VAR), Multiple Linear Regression, Gated Recurrent Unit (GRU) and Bidirectional LSTM (BiLSTM) has been experimented on two datasets. The experimental results show that after imputing missing values using RFR, the Stacked LSTM is optimized model for better performance than above mentioned reference models. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Interactive Multimedia & Artificial Intelligence is the property of Universidad Internacional de La Rioja (UNIR) 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: Stacked LSTM for Short-Term Wind Power Forecasting Using Multivariate Time Series Data.
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  Data: <searchLink fieldCode="AR" term="%22Galphade%2C+Manisha%22">Galphade, Manisha</searchLink><br /><searchLink fieldCode="AR" term="%22Nikam%2C+V%2E+B%2E%22">Nikam, V. B.</searchLink><br /><searchLink fieldCode="AR" term="%22Banerjee%2C+Biplab%22">Banerjee, Biplab</searchLink><br /><searchLink fieldCode="AR" term="%22Kiwelekar%2C+Arvind+W%2E%22">Kiwelekar, Arvind W.</searchLink><br /><searchLink fieldCode="AR" term="%22Sharma%2C+Priyanka%22">Sharma, Priyanka</searchLink>
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  Data: International Journal of Interactive Multimedia & Artificial Intelligence; Jun2025, Vol. 9 Issue 3, p71-81, 11p
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  Data: <searchLink fieldCode="DE" term="%22LONG+short-term+memory%22">LONG short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22WIND+forecasting%22">WIND forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22POWER+resources%22">POWER resources</searchLink><br /><searchLink fieldCode="DE" term="%22DEEP+learning%22">DEEP learning</searchLink><br /><searchLink fieldCode="DE" term="%22RENEWABLE+energy+sources%22">RENEWABLE energy sources</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Currently, wind power is the fast growing area in the domain of renewable energy generation. Accurate prediction of wind power output in wind farms is crucial for addressing the challenges associated the power grid. This precise forecasting enables grid operators to enhance safety and optimize grid operations by effectively managing fluctuations in power generation, ensuring a reliable and stable energy supply. In recent years, there has been a significant rise in research and investigations conducted in this field. This study aims to develop a multivariate short-term wind power forecasting (WPF) model with the objective of enhancing forecasting precision. Among the various prediction models, deep learning models such as Long Short-Term Memory (LSTM) have demonstrated outstanding performance in the field of WPF. By adding multiple layers of LSTM networks, the model can capture more complex patterns. To improve the performance, data preprocessing is carried out using two techniques such as removal of missing values and imputing missing values using Random Forest Regressor (RFR). The comparison between the proposed Stacked LSTM model and other methods including vector autoregressive (VAR), Multiple Linear Regression, Gated Recurrent Unit (GRU) and Bidirectional LSTM (BiLSTM) has been experimented on two datasets. The experimental results show that after imputing missing values using RFR, the Stacked LSTM is optimized model for better performance than above mentioned reference models. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Interactive Multimedia & Artificial Intelligence is the property of Universidad Internacional de La Rioja (UNIR) 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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      – Type: doi
        Value: 10.9781/ijimai.2024.07.002
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      – Code: eng
        Text: English
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        PageCount: 11
        StartPage: 71
    Subjects:
      – SubjectFull: LONG short-term memory
        Type: general
      – SubjectFull: WIND forecasting
        Type: general
      – SubjectFull: POWER resources
        Type: general
      – SubjectFull: DEEP learning
        Type: general
      – SubjectFull: RENEWABLE energy sources
        Type: general
    Titles:
      – TitleFull: Stacked LSTM for Short-Term Wind Power Forecasting Using Multivariate Time Series Data.
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            NameFull: Galphade, Manisha
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            NameFull: Banerjee, Biplab
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            NameFull: Kiwelekar, Arvind W.
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            NameFull: Sharma, Priyanka
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            – D: 01
              M: 06
              Text: Jun2025
              Type: published
              Y: 2025
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