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Improved Spatio-Temporal Linear Models for Very Short-Term Wind Speed Forecasting

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  • Tansu Filik

    (Department of Electrical and Electronics Engineering, Anadolu University, Eskisehir 26555, Turkey)

Abstract

In this paper, the spatio-temporal (multi-channel) linear models, which use temporal and the neighbouring wind speed measurements around the target location, for the best short-term wind speed forecasting are investigated. Multi-channel autoregressive moving average (MARMA) models are formulated in matrix form and efficient linear prediction coefficient estimation techniques are first used and revised. It is shown in detail how to apply these MARMA models to the spatially distributed wind speed measurements. The proposed MARMA models are tested using real wind speed measurements which are collected from the five stations around Canakkale region of Turkey. According to the test results, considerable improvements are observed over the well known persistence, autoregressive (AR) and multi-channel/vector autoregressive (VAR) models. It is also shown that the model can predict wind speed very fast (in milliseconds) which is suitable for the immediate short-term forecasting.

Suggested Citation

  • Tansu Filik, 2016. "Improved Spatio-Temporal Linear Models for Very Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 9(3), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:3:p:168-:d:65196
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    References listed on IDEAS

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    Cited by:

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    3. Hui Wang & Jingxuan Sun & Jianbo Sun & Jilong Wang, 2017. "Using Random Forests to Select Optimal Input Variables for Short-Term Wind Speed Forecasting Models," Energies, MDPI, vol. 10(10), pages 1-13, October.
    4. Mohammed Alzubaidi & Kazi N. Hasan & Lasantha Meegahapola & Mir Toufikur Rahman, 2021. "Identification of Efficient Sampling Techniques for Probabilistic Voltage Stability Analysis of Renewable-Rich Power Systems," Energies, MDPI, vol. 14(8), pages 1-15, April.
    5. Akçay, Hüseyin & Filik, Tansu, 2017. "Short-term wind speed forecasting by spectral analysis from long-term observations with missing values," Applied Energy, Elsevier, vol. 191(C), pages 653-662.
    6. Haixiang Zang & Miaomiao Wang & Jing Huang & Zhinong Wei & Guoqiang Sun, 2016. "A Hybrid Method for Generation of Typical Meteorological Years for Different Climates of China," Energies, MDPI, vol. 9(12), pages 1-19, December.
    7. Baïle, Rachel & Muzy, Jean-François, 2023. "Leveraging data from nearby stations to improve short-term wind speed forecasts," Energy, Elsevier, vol. 263(PA).

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