Hybrid LSTM-Based Fractional-Order Neural Network for Jeju Island’s Wind Farm Power Forecasting
Efficient integration of wind energy requires accurate wind power forecasting. This prediction is critical in optimising grid operation, energy trading, and effectively harnessing renewable resources. However, the wind’s complex and variable nature poses considerable challenges to achieving accurate...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Fractal and Fractional |
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| Online Access: | https://www.mdpi.com/2504-3110/8/3/149 |
| _version_ | 1797240974827585536 |
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| author | Bhukya Ramadevi Venkata Ramana Kasi Kishore Bingi |
| author_facet | Bhukya Ramadevi Venkata Ramana Kasi Kishore Bingi |
| author_sort | Bhukya Ramadevi |
| collection | DOAJ |
| description | Efficient integration of wind energy requires accurate wind power forecasting. This prediction is critical in optimising grid operation, energy trading, and effectively harnessing renewable resources. However, the wind’s complex and variable nature poses considerable challenges to achieving accurate forecasts. In this context, the accuracy of wind parameter forecasts, including wind speed and direction, is essential to enhancing the precision of wind power predictions. The presence of missing data in these parameters further complicates the forecasting process. These missing values could result from sensor malfunctions, communication issues, or other technical constraints. Addressing this issue is essential to ensuring the reliability of wind power predictions and the stability of the power grid. This paper proposes a long short-term memory (LSTM) model to forecast missing wind speed and direction data to tackle these issues. A fractional-order neural network (FONN) with a fractional arctan activation function is also developed to enhance generated wind power prediction. The predictive efficacy of the FONN model is demonstrated through two comprehensive case studies. In the first case, wind direction and forecast wind speed data are used, while in the second case, wind speed and forecast wind direction data are used for predicting power. The proposed hybrid neural network model improves wind power forecasting accuracy and addresses data gaps. The model’s performance is measured using mean errors and R<sup>2</sup> values. |
| first_indexed | 2024-04-24T18:15:58Z |
| format | Article |
| id | doaj.art-92a9fdbdf67f4c238d39ee95c3e1626b |
| institution | Directory Open Access Journal |
| issn | 2504-3110 |
| language | English |
| last_indexed | 2024-04-24T18:15:58Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fractal and Fractional |
| spelling | doaj.art-92a9fdbdf67f4c238d39ee95c3e1626b2024-03-27T13:42:04ZengMDPI AGFractal and Fractional2504-31102024-03-018314910.3390/fractalfract8030149Hybrid LSTM-Based Fractional-Order Neural Network for Jeju Island’s Wind Farm Power ForecastingBhukya Ramadevi0Venkata Ramana Kasi1Kishore Bingi2School of Electrical Engineering, Vellore Institute of Technology, Vellore 632014, IndiaSchool of Electrical Engineering, Vellore Institute of Technology, Vellore 632014, IndiaDepartment of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, MalaysiaEfficient integration of wind energy requires accurate wind power forecasting. This prediction is critical in optimising grid operation, energy trading, and effectively harnessing renewable resources. However, the wind’s complex and variable nature poses considerable challenges to achieving accurate forecasts. In this context, the accuracy of wind parameter forecasts, including wind speed and direction, is essential to enhancing the precision of wind power predictions. The presence of missing data in these parameters further complicates the forecasting process. These missing values could result from sensor malfunctions, communication issues, or other technical constraints. Addressing this issue is essential to ensuring the reliability of wind power predictions and the stability of the power grid. This paper proposes a long short-term memory (LSTM) model to forecast missing wind speed and direction data to tackle these issues. A fractional-order neural network (FONN) with a fractional arctan activation function is also developed to enhance generated wind power prediction. The predictive efficacy of the FONN model is demonstrated through two comprehensive case studies. In the first case, wind direction and forecast wind speed data are used, while in the second case, wind speed and forecast wind direction data are used for predicting power. The proposed hybrid neural network model improves wind power forecasting accuracy and addresses data gaps. The model’s performance is measured using mean errors and R<sup>2</sup> values.https://www.mdpi.com/2504-3110/8/3/149wind powerspeeddirectionfractional arctan functionLSTMfractional-order neural network |
| spellingShingle | Bhukya Ramadevi Venkata Ramana Kasi Kishore Bingi Hybrid LSTM-Based Fractional-Order Neural Network for Jeju Island’s Wind Farm Power Forecasting Fractal and Fractional wind power speed direction fractional arctan function LSTM fractional-order neural network |
| title | Hybrid LSTM-Based Fractional-Order Neural Network for Jeju Island’s Wind Farm Power Forecasting |
| title_full | Hybrid LSTM-Based Fractional-Order Neural Network for Jeju Island’s Wind Farm Power Forecasting |
| title_fullStr | Hybrid LSTM-Based Fractional-Order Neural Network for Jeju Island’s Wind Farm Power Forecasting |
| title_full_unstemmed | Hybrid LSTM-Based Fractional-Order Neural Network for Jeju Island’s Wind Farm Power Forecasting |
| title_short | Hybrid LSTM-Based Fractional-Order Neural Network for Jeju Island’s Wind Farm Power Forecasting |
| title_sort | hybrid lstm based fractional order neural network for jeju island s wind farm power forecasting |
| topic | wind power speed direction fractional arctan function LSTM fractional-order neural network |
| url | https://www.mdpi.com/2504-3110/8/3/149 |
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