Datasets / Solar: monthly and annual average direct normal (DNI), global horizontal (GHI), latitude tilt, and diffuse data and GIS data at 40km resolution for Africa from NREL


Solar: monthly and annual average direct normal (DNI), global horizontal (GHI), latitude tilt, and diffuse data and GIS data at 40km resolution for Africa from NREL

Published By Department of Energy

Issued over 9 years ago

US
beta

Summary

Type of release
a one-off release of a set of related datasets

Licence
Creative Commons CCZero

Verification
automatically awarded

Description

_(Abstract):_ Monthly Average Solar Resource for horizontal and tilted flat-plates, and 2-axis tracking concentrating collectors. _(Purpose):_ Provide information on the solar resource potential for the data domain. The insolation values represent the average solar energy available to solar collectors. _(Supplemental Information):_ These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The data were developed from [NREL's](http://http://en.openei.org/wiki/NREL "NREL's OpenEI page") Climatological Solar Radiation (CSR) Model. This model uses information on cloud cover, atmospheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface. Existing ground measurement stations are used to validate the data where possible. The modeled values are accurate to approximately 10% of a true measured value within the grid cell due to the uncertainties associated with meteorological input to the model. The local cloud cover can vary significantly even within a single grid cell as a result of terrain effects and other microclimate influences. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain.