Data-driven prediction of mean wind turbulence from topographic data

Bernardo Morais da Costa, Jonas Thor Snæbjörnsson, Ole Andre Øiseth, Jungao Wang, Jasna Bogunovic Jakobsen

Research output: Contribution to journalArticlepeer-review


Abstract This study presents a data-driven model to predict mean turbulence intensities at desired generic locations, for all wind directions. The model, a multilayer perceptron, requires only information about the local topography and a historical dataset of wind measurements and topography at other locations. Five years of data from six different wind measurement mast locations were used. A k-fold cross-validation evaluated the model at each location, where four locations were used for the training data, another location was used for validation, and the remaining one to test the model. The model outperformed the approach given in the European standard, for both performance metrics used. The results of different hyperparameter optimizations are presented, allowing for uncertainty estimates of the model performances.
Original languageEnglish
JournalIOP Conference Series: Materials Science and Engineering
Publication statusPublished - 1 Nov 2021


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