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The Use of Gridded Model Output Statistics ( GMOS ) in Energy Forecasting of a Solar Car

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  • Christiaan Oosthuizen

    (Department of Mechanical Engineering, Mechatronics and Industrial Design, Tshwane University of Technology, Pretoria 0001, South Africa
    Laboratoire d’Ingenierie des Systemes de Versailles (LISV), Universite Paris-Saclay, 78000 Paris, France)

  • Barend Van Wyk

    (Faculty of Engineering, the Built Environment and Technology, Nelson Mandela University, Port Elizabeth 6001, South Africa)

  • Yskandar Hamam

    (Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0001, South Africa)

  • Dawood Desai

    (Department of Mechanical Engineering, Mechatronics and Industrial Design, Tshwane University of Technology, Pretoria 0001, South Africa)

  • Yasser Alayli

    (Laboratoire d’Ingenierie des Systemes de Versailles (LISV), Universite Paris-Saclay, 78000 Paris, France)

Abstract

For many years, primary weather forecasting services (Global Forecast System (GFS) and the European Centre for Medium-Range Weather Forecasts (ECMWF)) have been made available to the public through global Numerical Weather Prediction (NWP) models estimating a multitude of general weather variables in a variety of resolutions. Secondary services such as weather experts Meteomatics AG use data and improve the forecasts through various methods. They tailor for the specific needs of customers in the wind and solar power generation sector as well as data scientists, analysts, and meteorologists in all areas of business. These auxiliary services have improved performance and provide reliable data. However, this work extended these auxiliary services to so-called tertiary services in which the weather forecasts were further conditioned for the very niche application environment of mobile solar technology in solar car energy management. The Gridded Model Output Statistics ( GMOS ) Global Horizontal Irradiance ( GHI ) model developed in this work utilizes historical data from various ground station locations in South Africa to reduce the mean forecast error of the GHI component. An average Root Mean Square Error ( RMSE) improvement of 11.28% was shown across all locations and weather conditions. It was also shown how the incorporation of the GMOS model could have increased the accuracy in regard to the State of Charge (SoC) energy simulation of a solar car during the Sasol Solar Challenge 2018 and the possible range benefits thereof.

Suggested Citation

  • Christiaan Oosthuizen & Barend Van Wyk & Yskandar Hamam & Dawood Desai & Yasser Alayli, 2020. "The Use of Gridded Model Output Statistics ( GMOS ) in Energy Forecasting of a Solar Car," Energies, MDPI, vol. 13(8), pages 1-16, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:8:p:1984-:d:346639
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    References listed on IDEAS

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