A global annual optimum tilt angle model for photovoltaic generation to use in the absence of local meteorological data

Carolina Nicolás-Martín, David Santos-Martín, Mónica Chinchilla-Sánchez and Scott Lemon

Renewable Energy, 2020, vol. 161, issue C, 722-735

Abstract: This manuscript proposes a series of global models to estimate optimum annual tilt angle (βopt) as a function of local variables (latitude, diffuse fraction and albedo) based on the hourly irradiance data of 14,468 sites spread across the globe from the One Building database. As a result, these models can be used for any location in the absence of local meteorological data. First, a polynomial regression model, applicable worldwide, is proposed to estimate βopt as a function of latitude. This model fits the global data considered with a 2% RMSE error. Average energy losses are estimated to be 1% for a 10° variation from βopt. A variation of 40° with respect to βopt, implies a 12–18% energy loss depending on latitude. In addition, if only latitude is considered to estimate βopt, different expressions should be used for latitudes >50° depending on the hemisphere. These variations are a result of the influence of diffuse irradiance on βopt, due to the fact that sites with higher amounts of diffuse irradiance have a lower βopt. Secondly, a polynomial surface regression model to estimate βopt as a function of latitude and the annual diffuse fraction is proposed improving the results, reaching a 0.7% RMSE error. Thirdly, a simplified polynomial surface regression model to estimate βopt as a function of latitude and albedo (without the influence of the diffuse fraction) is proposed, and finally a model gathering all three variables under study (latitude, annual diffuse fraction and albedo) to calculate the optimum tilt angle is presented.

Keywords: Diffuse fraction; Latitude; Optimum tilt angle; Photovoltaic energy; Albedo (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:161:y:2020:i:c:p:722-735

DOI: 10.1016/j.renene.2020.07.098

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