# Fast Numerical Wind Turbine Candidate Site Evaluation
## 1 Introduction
The proposed solution avoids the wait through measuring for a shorter period and using the collected measurements to train a model that is then able to predict the wind speed at the site for a longer period in the past. The model predictions can inform the turbine placement decisions, provided that they are close enough to the real wind speed values. Several different linear and nonlinear modelling methods are used, and results they provide are evaluated. Even though the wind speed is available as a NWP grid cell variable, experimental modelling is necessary to obtain its local value, because NWP discretization is coarse as compared to the length scale of interest. The use of modelling to obtain local weather variables from NWP predictions is established and it is called model output statistics (MOS) [21]. MOS has been successfully used for wind power forecasting and nowcasting [22; 23; 24] and for wind resource assessment [25; 26; 27], but not for site evaluation. When compared to typical MOS studies, the original contributions of the manuscript are the use of short training data sets, improving MOS through the use of measurements from the meteorological stations in the vicinity, and evaluation of the results from the perspective of wind turbine placement by emphasizing the predicted wind speed distribution. The shortness of the training data sets reflects the fact that the time used for data collection delays the subsequent stages of the project. The work is significant, since wind power, including small wind turbines, is popular and the work proposes a way for shortening the delay between the start of measuring and the turbine installation. No similar solution seems to have been suggested before. The proposed method is better suited for small wind turbines than for large ones for several reasons. Small wind turbines tend to be installed in places with low wind speeds [28], especially in off-grid applications [29], and at smaller heights. NWP signals predict the wind speed at these locations less accurately than high above a flatter, windier terrain. While large wind turbines may be located in a wind farm and distributed so as to optimally use the available area, economic reasons, such as land ownership, are likely to limit a small wind turbine to an exact location, aligning it with the assumptions of the method. In addition, it is easier to envisage making the decision to build a wind turbine based on a short period of local wind measurements when the turbine in question is small. Crucially for future practical application of the work, the proposed experimental modelling method is easy to use and no special modelling skills are required for utilizing it (the best performance is achieved with the most usual linear regression fitted with least squares using a few straightforward independent variables). Thus, its use would fit into the small planning budget for a small wind turbine. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Addressed Need** & **Location?** &
\begin{tabular}{l} **Real-Time** **Output** \\ **or Historic Data?** \\ \end{tabular} & **Local Measurements?** \\ \hline Wind resource assessment & area & historic & spatially limited \\ Wind power forecasting & point & real-time & available \\ Candidate site evaluation & point & historic & **available * \\ \hline \hline \end{tabular}  
* **The proposed solution enables evaluation with temporally limited local measurements.**  
\end{table} 
Table 1: Requirements for fulfilling different needs in wind power. Wind resource assessment characterizes the resource over an area, is performed on historical data, and has long-term time series data available at most for a limited set of locations in the area. Wind power forecasting addresses a selected site, has to work in real time, and it has real-time measurements from the site available. Evaluating a candidate site is done for a selected site on historical data.