West African operational daily solar forecast errors and their links with meteorological conditions

With its commitment to reduce greenhouse gas emissions and harnessing the potential of renewable energy, the West African region is at the forefront of global environmental challenges. This work focuses on the specific aspect of solar energy, which holds significant promise in the region. High quality solar energy forecasts are necessary for solar plants and power systems management, while they remain poorly developed in this region, in particular because of the specificities of the West African climate. We evaluate the errors in Global Horizontal Irradiance (GHI) operational forecast models for two Sahelian solar power plants, Zagtouli in Burkina Faso and Sococim in Senegal, and investigate their links with local meteorological conditions, with a specific focus on clouds and dust aerosols.

This work begins by assessing aerosol products and our results support the use of the CAMS reanalysis for the assessment of Aerosol Optical Depth (AOD), particularly with respect to dust aerosols. We then assess the performance of three operational GHI forecast products: the Global Forecast System (GFS, NCEP/NOAA), the Integrated Forecast System (IFS, ECMWF), and SteadyMet (SM), developed by French company Steadysun, which is computed from the previously mentioned Numerical Weather Prediction (NWP) model outputs. The analysis reveals that IFS and SM outperform GFS in terms of forecast accuracy, with SM showing a slight advantage due to its probabilistic nature, which provides valuable information on forecast uncertainty.

Closer examination reveals a significant relationship between GHI forecast errors and local meteorological characteristics. These errors are more pronounced during the wet season, primarily attributed to cloud occurrence. Dust events are found to play a secondary role, particularly during the dry season. Correlation analyses underline the main link between forecast errors and cloudiness, while co-occurrence analyses highlight the fact that dust aerosol loading is a secondary factor in forecast errors for the GHI directly or for cloud representation (aerosol-cloud interaction).