Wind speed prediction using LSTM and ARIMA time series analysis models: A case study of Gelibolu

Adem Demirtop; Onur Sevli

Makale detayları

Wind speed prediction using LSTM and ARIMA time series analysis models: A case study of Gelibolu

Yazarlar:

Adem Demirtop | Onur Sevli

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Clean Production Technologies | Wind

Dergi:

Turkish Journal of Engineering Vol.8 Issue 3, pp.524-536

Belge Tipi:

Makale

Yayın Tarihi:

7/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001322590

pISSN:

2587-1366

Anahtar kelimeler:

Özet:

 Wind energy stands out as a prominent renewable energy source, characterized by its high efficiency, feasibility, and wide applicability. Nonetheless, the integration of wind energy into the electrical system encounters significant obstacles due to the unpredictability and variability of wind speed. Accurate wind speed prediction is essential for estimating the short-, medium-, and long-term power output of wind turbines. Various methodologies and models exist for wind speed time series prediction. This research paper proposes a combination of two approaches to enhance forecasting accuracy: deep learning, particularly Long Short-Term Memory LSTM, and the Autoregressive Integrated Moving Average ARIMA model. LSTM, by retaining patterns over longer periods, improves prediction rates. Meanwhile, the ARIMA model enhances the likelihood of staying within predefined boundaries. The study utilizes daily average wind speed data from the Gelibolu district of Çanakkale province spanning 2014 to 2021. Evaluation using the root mean square error RMSE shows the superior forecast accuracy of the LSTM model compared to ARIMA. The LSTM model achieved an RMSE of 6.3 and a mean absolute error of 16.67. These results indicate the potential utility of the proposed approach in wind speed forecasting, offering performance comparable to or exceeding other studies in the literature.

 Wind energy stands out as a prominent renewable energy source, characterized by its high efficiency, feasibility, and wide applicability. Nonetheless, the integration of wind energy into the electrical system encounters significant obstacles due to the unpredictability and variability of wind speed. Accurate wind speed prediction is essential for estimating the short-, medium-, and long-term power output of wind turbines. Various methodologies and models exist for wind speed time series prediction. This research paper proposes a combination of two approaches to enhance forecasting accuracy: deep learning, particularly Long Short-Term Memory LSTM, and the Autoregressive Integrated Moving Average ARIMA model. LSTM, by retaining patterns over longer periods, improves prediction rates. Meanwhile, the ARIMA model enhances the likelihood of staying within predefined boundaries. The study utilizes daily average wind speed data from the Gelibolu district of Çanakkale province spanning 2014 to 2021. Evaluation using the root mean square error RMSE shows the superior forecast accuracy of the LSTM model compared to ARIMA. The LSTM model achieved an RMSE of 6.3 and a mean absolute error of 16.67. These results indicate the potential utility of the proposed approach in wind speed forecasting, offering performance comparable to or exceeding other studies in the literature.

 Wind energy stands out as a prominent renewable energy source, characterized by its high efficiency, feasibility, and wide applicability. Nonetheless, the integration of wind energy into the electrical system encounters significant obstacles due to the unpredictability and variability of wind speed. Accurate wind speed prediction is essential for estimating the short-, medium-, and long-term power output of wind turbines. Various methodologies and models exist for wind speed time series prediction. This research paper proposes a combination of two approaches to enhance forecasting accuracy: deep learning, particularly Long Short-Term Memory LSTM, and the Autoregressive Integrated Moving Average ARIMA model. LSTM, by retaining patterns over longer periods, improves prediction rates. Meanwhile, the ARIMA model enhances the likelihood of staying within predefined boundaries. The study utilizes daily average wind speed data from the Gelibolu district of Çanakkale province spanning 2014 to 2021. Evaluation using the root mean square error RMSE shows the superior forecast accuracy of the LSTM model compared to ARIMA. The LSTM model achieved an RMSE of 6.3 and a mean absolute error of 16.67. These results indicate the potential utility of the proposed approach in wind speed forecasting, offering performance comparable to or exceeding other studies in the literature.

 Wind energy stands out as a prominent renewable energy source, characterized by its high efficiency, feasibility, and wide applicability. Nonetheless, the integration of wind energy into the electrical system encounters significant obstacles due to the unpredictability and variability of wind speed. Accurate wind speed prediction is essential for estimating the short-, medium-, and long-term power output of wind turbines. Various methodologies and models exist for wind speed time series prediction. This research paper proposes a combination of two approaches to enhance forecasting accuracy: deep learning, particularly Long Short-Term Memory LSTM, and the Autoregressive Integrated Moving Average ARIMA model. LSTM, by retaining patterns over longer periods, improves prediction rates. Meanwhile, the ARIMA model enhances the likelihood of staying within predefined boundaries. The study utilizes daily average wind speed data from the Gelibolu district of Çanakkale province spanning 2014 to 2021. Evaluation using the root mean square error RMSE shows the superior forecast accuracy of the LSTM model compared to ARIMA. The LSTM model achieved an RMSE of 6.3 and a mean absolute error of 16.67. These results indicate the potential utility of the proposed approach in wind speed forecasting, offering performance comparable to or exceeding other studies in the literature.

Makale detayları

Wind speed prediction using LSTM and ARIMA time series analysis models: A case study of Gelibolu

Yazarlar:

Adem Demirtop | Onur Sevli

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Clean Production Technologies | Wind

Dergi:

Turkish Journal of Engineering Vol.8 Issue 3, pp.524-536

Belge Tipi:

Makale

Yayın Tarihi:

7/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001322590

pISSN:

2587-1366

Wind speed prediction

Wind energy

LSTM

ARIMA

Deep learning

Sayı

Makale detayları

Wind speed prediction using LSTM and ARIMA time series analysis models: A case study of Gelibolu

Yazarlar: Adem Demirtop | Onur Sevli

Yayıncı: Murat YAKAR

Ana Başlıklar: Clean Production Technologies | Wind

Dergi: Turkish Journal of Engineering Vol.8 Issue 3, pp.524-536

Belge Tipi: Makale

Yayın Tarihi: 7/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001322590

pISSN: 2587-1366

Wind speed prediction

Wind energy

LSTM

ARIMA

Deep learning

Sayı

Yazarlar:

Adem Demirtop | Onur Sevli

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Clean Production Technologies | Wind

Dergi:

Turkish Journal of Engineering Vol.8 Issue 3, pp.524-536

Belge Tipi:

Makale

Yayın Tarihi:

7/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001322590

pISSN:

2587-1366