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A univariate time series methodology based on sequence-to-sequence learning for short to midterm wind power production

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  • Akbal, Yıldırım
  • Ünlü, Kamil Demirberk

Abstract

The biggest wind farm of Turkey is placed at Manisa which is located in the Aegean Region. Electricity is a non-storable commodity for that reason, it is very important to have a strong forecast and model of the potential electricity production to plan the electricity loads. In this study, the aim is to model and forecast electricity production of the wind farms located at Manisa by using a univariate model based on sequence-to-sequence learning. The forecasting range of the study is from short term to midterm. The strength of the proposed model is that; it only needs its own lagged value to make forecasts. The empirical evidences show that the model has high coefficient of variation (R2) in short term and moderate R2 in the midterm forecast. Although in the mid-range forecasts R2 slightly decreases mean squared error and mean absolute error shows that the model is accurate also in the midterm forecasts. The proposed model is not only strong in hourly electricity production forecasts but with a slight modification also in forecasting the minimum, maximum and average electricity production for a fixed range. This study concludes with two fresh and intriguing future research ideas.

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  • Akbal, Yıldırım & Ünlü, Kamil Demirberk, 2022. "A univariate time series methodology based on sequence-to-sequence learning for short to midterm wind power production," Renewable Energy, Elsevier, vol. 200(C), pages 832-844.
    • Handle: RePEc:eee:renene:v:200:y:2022:i:c:p:832-844
    DOI: 10.1016/j.renene.2022.10.055
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    Cited by:

    1. Gökay Yörük & Ugur Bac & Fatma Yerlikaya-Özkurt & Kamil Demirberk Ünlü, 2023. "Strategic Electricity Production Planning of Turkey via Mixed Integer Programming Based on Time Series Forecasting," Mathematics, MDPI, vol. 11(8), pages 1-20, April.
    2. Bashir, Hassan & Sibtain, Muhammad & Hanay, Özge & Azam, Muhammad Imran & Qurat-ul-Ain, & Saleem, Snoober, 2023. "Decomposition and Harris hawks optimized multivariate wind speed forecasting utilizing sequence2sequence-based spatiotemporal attention," Energy, Elsevier, vol. 278(PB).
    3. Yan, Jie & Nuertayi, Akejiang & Yan, Yamin & Liu, Shan & Liu, Yongqian, 2023. "Hybrid physical and data driven modeling for dynamic operation characteristic simulation of wind turbine," Renewable Energy, Elsevier, vol. 215(C).
    4. Aamer A. Shah & Almani A. Aftab & Xueshan Han & Mazhar Hussain Baloch & Mohamed Shaik Honnurvali & Sohaib Tahir Chauhdary, 2023. "Prediction Error-Based Power Forecasting of Wind Energy System Using Hybrid WT–ROPSO–NARMAX Model," Energies, MDPI, vol. 16(7), pages 1-15, April.
    5. Ejigu Tefera Habtemariam & Kula Kekeba & María Martínez-Ballesteros & Francisco Martínez-Álvarez, 2023. "A Bayesian Optimization-Based LSTM Model for Wind Power Forecasting in the Adama District, Ethiopia," Energies, MDPI, vol. 16(5), pages 1-22, February.

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