Hide/Show Apps

A Comparative Study of virtual and operational met mast data

Orhan, O. Emre
Ahmet, Gokhan
Performance of wind assessment studies depend on the adequacy and duration of the wind data. For a reasonable wind assessment, at least one full year wind data is needed so that, all the variations throughout the year are represented. On the other hand, it is always a question of time and cost how to get the wind data. On-site measurements are the most common way of obtaining wind data but it is the most expensive and time consuming as well. Apart from on-site data, there are also reanalysis long term data sources like MERRA, NCAR, etc. Time and spatial resolution of these long term data are lower compared to on-site measurements but in cases where on-site measurements are not available, they are also utilized. On top of on-site and reanalysis wind data, weather forecasting models like WRF, MM5 are available. Although, these models mainly are used for forecasting services, flexibility of the models makes them suitable for preliminary resource assessment purposes. In this study, comparisons of annual energy production estimations are computed using virtual and on-site met mast data separately for a specific time range. The widely used weather research and forecasting model (WRF) is used to provide virtual met mast data. Once WRF simulations are completed, interpolation routines are employed in order to extract data for a specific location. The on-site met mast is located inside a wind farm project area which is under development. Project site is located in the south of Turkey. There are four different met masts, three of them recording wind data presently. On-site measurements together with WRF results are used to obtain energy yields for the project area. The performance of both methodologies is compared. It has been observed that WRF can as well serve as a preliminary model in cases where no other data source is available but the model has to be implemented with great care depending on the project site conditions.