Towards efficient wind energy monitoring: Learning more from open source data

Europe’s massive shift towards sustainable energy production has triggered a variety of new large scale projects, and wind energy is a crucial part of the effort to achieve a carbon-free future. However, because of their low financial impact and relatively high measurement campaign costs, small scale projects are often deemed impractical beforehand. To help small communities gain insight on the wind energy conditions in their surroundings, the present work briefly introduces a measuring station (MEST) concept based on affordable electronic components and proposes a solution to alleviating the effects of inevitable measurement data inconsistency on the energy yield analysis. By leveraging open source machine learning models and establishing a link with the publicly available ERA5-Land climate database, missing wind speed measurement data is reconstructed at an accuracy of up to 0.11 $\frac{m}{s}$. The impact of data reconstruction on the estimated energy production of a wind turbine (WT) erected at the measuring location is then evaluated using the measurement data acquired by a MEST prototype and the ERA5-Land data recorded during October and November 2019. The results indicate that at a location experiencing moderate wind speeds, the estimated energy output of the WT is increased by up to 2 % in comparison with other data analysis procedures. Although the minute underestimation is not of great importance to the success of the analysis, the inaccuracies at higher wind speeds have a far more profound effect on the WT’s estimated energy output, and they can stop a potentially successful wind energy project from gaining further attention.