The Use of Gridded Model Output Statistics (<i>GMOS</i>) in Energy Forecasting of a Solar Car
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 i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/8/1984 |
| _version_ | 1827718464161185792 |
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| author | Christiaan Oosthuizen Barend Van Wyk Yskandar Hamam Dawood Desai Yasser Alayli |
| author_facet | Christiaan Oosthuizen Barend Van Wyk Yskandar Hamam Dawood Desai Yasser Alayli |
| author_sort | Christiaan Oosthuizen |
| collection | DOAJ |
| description | 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 (<i>GMOS</i>) Global Horizontal Irradiance (<i>GHI</i>) model developed in this work utilizes historical data from various ground station locations in South Africa to reduce the mean forecast error of the <i>GHI</i> component. An average Root Mean Square Error (<i>RMSE)</i> improvement of 11.28% was shown across all locations and weather conditions. It was also shown how the incorporation of the <i>GMOS</i> 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. |
| first_indexed | 2024-03-10T20:25:20Z |
| format | Article |
| id | doaj.art-970d0cd191924718a4f79ea131138156 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T20:25:20Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-970d0cd191924718a4f79ea1311381562023-11-19T21:52:29ZengMDPI AGEnergies1996-10732020-04-01138198410.3390/en13081984The Use of Gridded Model Output Statistics (<i>GMOS</i>) in Energy Forecasting of a Solar CarChristiaan Oosthuizen0Barend Van Wyk1Yskandar Hamam2Dawood Desai3Yasser Alayli4Department of Mechanical Engineering, Mechatronics and Industrial Design, Tshwane University of Technology, Pretoria 0001, South AfricaFaculty of Engineering, the Built Environment and Technology, Nelson Mandela University, Port Elizabeth 6001, South AfricaDepartment of Electrical Engineering, Tshwane University of Technology, Pretoria 0001, South AfricaDepartment of Mechanical Engineering, Mechatronics and Industrial Design, Tshwane University of Technology, Pretoria 0001, South AfricaLaboratoire d’Ingenierie des Systemes de Versailles (LISV), Universite Paris-Saclay, 78000 Paris, FranceFor 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 (<i>GMOS</i>) Global Horizontal Irradiance (<i>GHI</i>) model developed in this work utilizes historical data from various ground station locations in South Africa to reduce the mean forecast error of the <i>GHI</i> component. An average Root Mean Square Error (<i>RMSE)</i> improvement of 11.28% was shown across all locations and weather conditions. It was also shown how the incorporation of the <i>GMOS</i> 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.https://www.mdpi.com/1996-1073/13/8/1984Global Horizontal Irradianceweather forecastingstate of chargeenergymodel output statisticssolar electric car |
| spellingShingle | Christiaan Oosthuizen Barend Van Wyk Yskandar Hamam Dawood Desai Yasser Alayli The Use of Gridded Model Output Statistics (<i>GMOS</i>) in Energy Forecasting of a Solar Car Energies Global Horizontal Irradiance weather forecasting state of charge energy model output statistics solar electric car |
| title | The Use of Gridded Model Output Statistics (<i>GMOS</i>) in Energy Forecasting of a Solar Car |
| title_full | The Use of Gridded Model Output Statistics (<i>GMOS</i>) in Energy Forecasting of a Solar Car |
| title_fullStr | The Use of Gridded Model Output Statistics (<i>GMOS</i>) in Energy Forecasting of a Solar Car |
| title_full_unstemmed | The Use of Gridded Model Output Statistics (<i>GMOS</i>) in Energy Forecasting of a Solar Car |
| title_short | The Use of Gridded Model Output Statistics (<i>GMOS</i>) in Energy Forecasting of a Solar Car |
| title_sort | use of gridded model output statistics i gmos i in energy forecasting of a solar car |
| topic | Global Horizontal Irradiance weather forecasting state of charge energy model output statistics solar electric car |
| url | https://www.mdpi.com/1996-1073/13/8/1984 |
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