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A Novel Approach for Wind Speed Forecasting Using LSTM-ARIMA Deep Learning Models

Author

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  • Vikram Bali

    (JSS Academy of Technical Education, Noida, India)

  • Ajay Kumar

    (JSS Academy of Technical Education, Noida, India)

  • Satyam Gangwar

    (JSS Academy of Technical Education, Noida, India)

Abstract

The term which is used to predict wind speed to produce wind power is wind speed forecasting. Deep learning, is a form of AI, basically indulging in artificial intelligence and thus can greatly increase the precision rate on larger datasets. In this research paper, the two techniques are being used together to obtain the better forecasting results. Both the techniques are forecasting based and combining LSTM and deep learning can increase the forecast rate because of the pattern remembering attribute of LSTM over a longer interval/period of time. If there is the inclusion of the ARIMA model the likelihood of a future value lying between two indicated limits is increased. So, overall if both the techniques are hybridized than it is most probable that the obtained results should be more accurate than both the techniques used separately. So, the main focus of this research article is on the efficiency and evaluation of hybridized LSTM-ARIMA model to predict wind speed forecasting.

Suggested Citation

  • Vikram Bali & Ajay Kumar & Satyam Gangwar, 2020. "A Novel Approach for Wind Speed Forecasting Using LSTM-ARIMA Deep Learning Models," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global, vol. 11(3), pages 13-30, July.
    • Handle: RePEc:igg:jaeis0:v:11:y:2020:i:3:p:13-30
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    Cited by:

    1. Balkissoon, Sarah & Fox, Neil & Lupo, Anthony & Haupt, Sue Ellen & Penny, Stephen G., 2023. "Classification of tall tower meteorological variables and forecasting wind speeds in Columbia, Missouri," Renewable Energy, Elsevier, vol. 217(C).
    2. Kamil Kashif & Robert 'Slepaczuk, 2024. "LSTM-ARIMA as a Hybrid Approach in Algorithmic Investment Strategies," Papers 2406.18206, arXiv.org.
    3. Weiqian Zhang & Songsong Li & Zhichang Guo & Yizhe Yang, 2023. "A hybrid forecasting model based on deep learning feature extraction and statistical arbitrage methods for stock trading strategies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1729-1749, November.

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