Development and validation of a new efficient SRTM DEM-based horizon model combined with optimization and error prediction methods. (May 2016)

• Record Type: Journal Article
• Authors: Pillot, Benjamin; Muselli, Marc; Poggi, Philippe; Haurant, Pierrick; Dias, João Batista
• Is Part Of: Solar energy. Volume 129(2016)
• Page Start: 101
• Page End: 115
• Publication Date: 2016-05
• Subjects: Horizon model -- SRTM DEM -- Solar radiation maps -- Error prediction; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2016.01.058
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: We have developed a general horizon model based on the SRTM DEM. It is intended to efficiently consider topography in large solar radiation maps. Comparison with in situ data and other models has validated efficiency of our model. A method for knowingly altering precision and improving computation time is proposed. An empirical study has been performed for predicting horizon DEM-based error. Abstract: Several post-processing methods have been developed over the last years in order to take into consideration topography within satellite-based solar radiation maps using digital elevation models (DEM). If the main part of these procedures is to estimate the obstructed horizon around each DEM point of a given region so as to consider terrain-based shading effects, the size of the area can also limit this implementation. That is why we have developed a new efficient horizon model based on the DEM retrieved during the Shuttle Radar Topography Mission (SRTM). In order to be usable at any world location with the same expected accuracy, this model is only derived from mathematical statements without any kind of empirical approximation. Validation against in situ horizons and comparison with some other models have finally shown this one presents both better accuracy (RMSE of 1.555 ° against 1.712 ° or more) and lower computation time (at least 4 times faster). Furthermore, in the case of very large areas, we propose an optimization procedure allowing the user to knowingly alter the modeling error in order to reduce processing time. Finally, using in situ data, we have also developed a method for predicting the repercussion of the original SRTM DEM error on the final horizon precision.