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ForecastTB—An R Package as a Test-Bench for Time Series Forecasting—Application of Wind Speed and Solar Radiation Modeling

Author

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  • Neeraj Dhanraj Bokde

    (Department of Engineering—Renewable Energy and Thermodynamics, Aarhus University, 8000 Aarhus, Denmark)

  • Zaher Mundher Yaseen

    (Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam)

  • Gorm Bruun Andersen

    (Department of Engineering—Renewable Energy and Thermodynamics, Aarhus University, 8000 Aarhus, Denmark)

Abstract

This paper introduces an R package ForecastTB that can be used to compare the accuracy of different forecasting methods as related to the characteristics of a time series dataset. The ForecastTB is a plug-and-play structured module, and several forecasting methods can be included with simple instructions. The proposed test-bench is not limited to the default forecasting and error metric functions, and users are able to append, remove, or choose the desired methods as per requirements. Besides, several plotting functions and statistical performance metrics are provided to visualize the comparative performance and accuracy of different forecasting methods. Furthermore, this paper presents real application examples with natural time series datasets (i.e., wind speed and solar radiation) to exhibit the features of the ForecastTB package to evaluate forecasting comparison analysis as affected by the characteristics of a dataset. Modeling results indicated the applicability and robustness of the proposed R package ForecastTB for time series forecasting.

Suggested Citation

  • Neeraj Dhanraj Bokde & Zaher Mundher Yaseen & Gorm Bruun Andersen, 2020. "ForecastTB—An R Package as a Test-Bench for Time Series Forecasting—Application of Wind Speed and Solar Radiation Modeling," Energies, MDPI, vol. 13(10), pages 1-24, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:10:p:2578-:d:360302
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    References listed on IDEAS

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    8. Hai Tao & Sadeq Oleiwi Sulaiman & Zaher Mundher Yaseen & H. Asadi & Sarita Gajbhiye Meshram & M. A. Ghorbani, 2018. "What Is the Potential of Integrating Phase Space Reconstruction with SVM-FFA Data-Intelligence Model? Application of Rainfall Forecasting over Regional Scale," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(12), pages 3935-3959, September.
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    Cited by:

    1. Ali O. Al-Sulttani & Amimul Ahsan & Basim A. R. Al-Bakri & Mahir Mahmod Hason & Nik Norsyahariati Nik Daud & S. Idrus & Omer A. Alawi & Elżbieta Macioszek & Zaher Mundher Yaseen, 2022. "Double-Slope Solar Still Productivity Based on the Number of Rubber Scraper Motions," Energies, MDPI, vol. 15(21), pages 1-34, October.
    2. Bokde, Neeraj Dhanraj & Tranberg, Bo & Andresen, Gorm Bruun, 2021. "Short-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling," Applied Energy, Elsevier, vol. 281(C).
    3. Isabella Yunfei Zeng & Shiqi Tan & Jianliang Xiong & Xuesong Ding & Yawen Li & Tian Wu, 2021. "Estimation of Real-World Fuel Consumption Rate of Light-Duty Vehicles Based on the Records Reported by Vehicle Owners," Energies, MDPI, vol. 14(23), pages 1-19, November.

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