Development of an Algorithm for Prediction of the Wind Speed in Renewable Energy Environments

The aim of this work is to develop an algorithm that is able to provide predictions of wind speed statistics (WSS) in renewable energy environments. The subject is clearly interesting, as predictions of storms and extreme winds are important for decision makers and emergency response teams in renewable energy environments, e.g., in places where wind turbines could be located, including cities. The goal of the work is achieved through two phases: (a) During the preparation phase, the construction of a big WSS database based on computational fluid dynamics (CFD) is carried out, which includes flow fields of different wind directions in all grid numerical points; (b) In the second phase, the algorithm is used to find the records in the WSS database with the closest meteorological conditions to the meteorological conditions of interest. The evaluation of the CFD model (including both RANS and LES turbulence methodologies) is performed using the experimental data of the MUST (Mock Urban Setting Test) wind tunnel experiment.

Automated summary

The aim of this study is to develop an algorithm for predicting wind speed statistics in renewable energy environments, achieved through two phases including the construction of a WSS database and using the algorithm to find records with similar meteorological conditions. The CFD model, including RANS and LES turbulence methodologies, is evaluated using experimental data from the MUST wind tunnel experiment.