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Day-Ahead Forecasting of the Percentage of Renewables Based on Time-Series Statistical Methods

Author

Listed:
  • Robert Basmadjian

    (Department of Informatics, Clausthal University of Technology, Julius-Albert-Str. 4, 38678 Clausthal-Zellerfeld, Germany)

  • Amirhossein Shaafieyoun

    (ONELOGIC GmbH, Kapuzinerstraße 2c, 94032 Passau, Germany)

  • Sahib Julka

    (Chair of Data Science, University of Passau, Innstrasse 43, 94032 Passau, Germany)

Abstract

Forecasting renewable energy sources is of critical importance to several practical applications in the energy field. However, due to the inherent volatile nature of these energy sources, doing so remains challenging. Numerous time-series methods have been explored in literature, which consider only one specific type of renewables (e.g., solar or wind), and are suited to small-scale (micro-level) deployments. In this paper, the different types of renewable energy sources are reflected, which are distributed at a national level (macro-level). To generate accurate predictions, a methodology is proposed, which consists of two main phases. In the first phase, the most relevant variables having impact on the generation of the renewables are identified using correlation analysis. The second phase consists of (1) estimating model parameters, (2) optimising and reducing the number of generated models, and (3) selecting the best model for the method under study. To this end, the three most-relevant time-series auto-regression based methods of SARIMAX, SARIMA, and ARIMAX are considered. After deriving the best model for each method, then a comparison is carried out between them by taking into account different months of the year. The evaluation results illustrate that our forecasts have mean absolute error rates between 6.76 and 11.57%, while considering both inter- and intra-day scenarios. The best models are implemented in an open-source REN4Kast software platform.

Suggested Citation

  • Robert Basmadjian & Amirhossein Shaafieyoun & Sahib Julka, 2021. "Day-Ahead Forecasting of the Percentage of Renewables Based on Time-Series Statistical Methods," Energies, MDPI, vol. 14(21), pages 1-23, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7443-:d:674699
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    References listed on IDEAS

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    1. González-Aparicio, I. & Zucker, A., 2015. "Impact of wind power uncertainty forecasting on the market integration of wind energy in Spain," Applied Energy, Elsevier, vol. 159(C), pages 334-349.
    2. Demirhan, Haydar & Renwick, Zoe, 2018. "Missing value imputation for short to mid-term horizontal solar irradiance data," Applied Energy, Elsevier, vol. 225(C), pages 998-1012.
    3. Peter Bauer & Alan Thorpe & Gilbert Brunet, 2015. "The quiet revolution of numerical weather prediction," Nature, Nature, vol. 525(7567), pages 47-55, September.
    4. Scheu, Matti Niclas & Kolios, Athanasios & Fischer, Tim & Brennan, Feargal, 2017. "Influence of statistical uncertainty of component reliability estimations on offshore wind farm availability," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 28-39.
    5. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    6. Majid Mehrasa & Edris Pouresmaeil & Bahram Pournazarian & Amir Sepehr & Mousa Marzband & João P. S. Catalão, 2018. "Synchronous Resonant Control Technique to Address Power Grid Instability Problems Due to High Renewables Penetration," Energies, MDPI, vol. 11(9), pages 1-18, September.
    7. Tomasz Ciechulski & Stanisław Osowski, 2020. "Deep Learning Approach to Power Demand Forecasting in Polish Power System," Energies, MDPI, vol. 13(22), pages 1-13, November.
    8. Tomasz Ciechulski & Stanisław Osowski, 2021. "High Precision LSTM Model for Short-Time Load Forecasting in Power Systems," Energies, MDPI, vol. 14(11), pages 1-15, May.
    9. Neves, Diana & Brito, Miguel C. & Silva, Carlos A., 2016. "Impact of solar and wind forecast uncertainties on demand response of isolated microgrids," Renewable Energy, Elsevier, vol. 87(P2), pages 1003-1015.
    10. Dengyong Zhang & Haixin Tong & Feng Li & Lingyun Xiang & Xiangling Ding, 2020. "An Ultra-Short-Term Electrical Load Forecasting Method Based on Temperature-Factor-Weight and LSTM Model," Energies, MDPI, vol. 13(18), pages 1-14, September.
    11. Robert Basmadjian, 2019. "Flexibility-Based Energy and Demand Management in Data Centers: A Case Study for Cloud Computing," Energies, MDPI, vol. 12(17), pages 1-22, August.
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