Measure-correlate-predict methods to improve the assessment of wind and wave energy availability at a semi-exposed coastal area

To reduce the carbon footprint of near-shore and land-based Recirculating Aquaculture Systems (RAS), there is interest to harness renewable energy from local sources. When estimating power availability from wind and waves, it is imperative to ensure that the metocean resource data accurately represents the energy flux at the specific site. Here, four measure-correlate-predict (MCP) models are investigated for wind speed, significant wave height and peak wave period. The MCP relationships are obtained from higher spatial resolution wind and wave data, from a weather research and forecasting model, met-mast measurements at Stord airport (wind), and spectral downscaled DNORA/SWAN model (wave), correlated to the NORA3 database over a common time interval. These relations are used to improve the predictions of metocean parameters from the NORA3 model at the RAS location for the period 2012–2021, and hence allow more accurate estimates of renewable power availability. The prediction accuracy of the MCP models improves in all four cases when an appropriate data sorting criterion and algorithm is implemented. Between 2012 and 2021, average wind power density and wave power density are lowest in July at 182 W/m2 and 0.6 kW/m, and highest in December at 538 W/m2 and 7.5 kW/m, respectively.