Assessing Predictions of Australian Offshore Wind Energy Resources from Reanalysis Datasets

Emily Cowin, Changlong Wang and Stuart D. C. Walsh ()
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Emily Cowin: Civil Engineering, Monash University, Clayton 3800, Australia
Changlong Wang: Civil Engineering, Monash University, Clayton 3800, Australia
Stuart D. C. Walsh: Civil Engineering, Monash University, Clayton 3800, Australia

Energies, 2023, vol. 16, issue 8, 1-21

Abstract: Offshore wind farms are a current area of interest in Australia due to their ability to support its transition to renewable energy. Climate reanalysis datasets that provide simulated wind speed data are frequently used to evaluate the potential of proposed offshore wind farm locations. However, there has been a lack of comparative studies of the accuracy of wind speed predictions from different reanalysis datasets for offshore wind farms in Australian waters. This paper assesses wind speed distribution accuracy and compares predictions of offshore wind turbine power output in Australia from three international reanalysis datasets: BARRA, ERA5, and MERRA-2. Pressure level data were used to determine wind speeds and capacity factors were calculated using a turbine bounding curve. Predictions across the datasets show consistent spatial and temporal variations in the predicted plant capacity factors, but the magnitudes differ substantially. Compared to weather station data, wind speed predictions from the BARRA dataset were found to be the most accurate, with a higher correlation and lower average error than ERA5 and MERRA-2. Significant variation was seen in predictions and there was a lack of similarity with weather station measurements, which highlights the need for additional site-based measurements.

Keywords: renewable resource estimation; energy transition; numerical analysis; offshore wind (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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