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A database infrastructure to implement real-time solar and wind power generation intra-hour forecasts

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  • Pedro, Hugo T.C.
  • Lim, Edwin
  • Coimbra, Carlos F.M.

Abstract

This paper presents a simple forecasting database infrastructure implemented using the open-source database management system MySQL. This proposal aims at advancing the myriad of solar and wind forecast models present in the literature into a production stage. The paper gives all relevant details necessary to implement a MySQL infra-structure that collects the raw data, filters unrealistic values, classifies the data, and produces forecasts automatically and without the assistance of any other computational tools. The performance of this methodology is demonstrated by creating intra-hour power output forecasts for a 1 MW photovoltaic installation in Southern California and a 10 MW wind power plant in Central California. Several machine learning forecast models are implemented (persistence, auto-regressive and nearest neighbors) and tested. Both point forecasts and prediction intervals are generated with this methodology. Quantitative and qualitative analyses of solar and wind power forecasts were performed for an extended testing period (4 years and 6 years, respectively). Results show an acceptable and robust performance for the proposed forecasts.

Suggested Citation

  • Pedro, Hugo T.C. & Lim, Edwin & Coimbra, Carlos F.M., 2018. "A database infrastructure to implement real-time solar and wind power generation intra-hour forecasts," Renewable Energy, Elsevier, vol. 123(C), pages 513-525.
    • Handle: RePEc:eee:renene:v:123:y:2018:i:c:p:513-525
    DOI: 10.1016/j.renene.2018.02.043
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    Cited by:

    1. Yang, Hufang & Jiang, Ping & Wang, Ying & Li, Hongmin, 2022. "A fuzzy intelligent forecasting system based on combined fuzzification strategy and improved optimization algorithm for renewable energy power generation," Applied Energy, Elsevier, vol. 325(C).
    2. Luis Mazorra-Aguiar & Philippe Lauret & Mathieu David & Albert Oliver & Gustavo Montero, 2021. "Comparison of Two Solar Probabilistic Forecasting Methodologies for Microgrids Energy Efficiency," Energies, MDPI, vol. 14(6), pages 1-26, March.
    3. Fatemi, Seyyed A. & Kuh, Anthony & Fripp, Matthias, 2018. "Parametric methods for probabilistic forecasting of solar irradiance," Renewable Energy, Elsevier, vol. 129(PA), pages 666-676.
    4. Yang, Dazhi & Wu, Elynn & Kleissl, Jan, 2019. "Operational solar forecasting for the real-time market," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1499-1519.

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