# Fast Numerical Wind Turbine Candidate Site Evaluation
## 1 Introduction
The inputs of the model are the signals that are available for the past independently from the wind project-- measurements from the meteorological stations in the vicinity and NWP signals. The trained model is then used as a soft sensor. It predicts the wind signal for a longer past period in which the wind speed at the site was not measured, but during which the model inputs are available. To evaluate the feasibility of such use of modelling, model predictions are compared to the measurements over a test period, which is separate from the training period. Several methods of linear and nonlinear modelling are tested and their suitability for the purpose is evaluated. The test locations are in a complex terrain with moderate winds. This is desirable, because models for such sites are needed--near-future turbine technology will allow harvesting of wind energy over low wind regions at wind speeds as low as 3 m/s [13]. Models have rarely addressed such areas because wind power forecasting has more often been used in flat terrain with strong persistent winds. Modelling a complex terrain is also a challenging test for the modelling method, which is beneficial. The requirements in candidate wind turbine site evaluation are somewhat different than in either wind power forecasting or in wind resource assessment. Recent wind power forecasting research \"can predict the fluctuation of output wind power in wind farms\" [14] and, therefore, helps to ensure safe operation of the power system [15]. Multi step forecast is achieved by singular spectrum analysis and a hybrid Laguerre neural network [14] or based on extreme learning machine in multi-objective optimization algorithm [16] or Convolutional Neural Network cascaded with a Radial Basis Function Neural Network [17]. The task of predicting local wind signals from other signals with a limited amount of training data introduces different constraints than wind power forecasting modelling:  
* The prediction is not real-time, eliminating the timing constraints--model inputs do not have to be available before the time for which the prediction is made. Reanalyses, which are more accurate, can be used. * No measurement of the wind parameters on the candidate site is available as a model input. The measurements are available as training data to train the model, but not as an input at times close to the time for which the prediction is made. The wind resource assessment models give the answer on the energy yields in the region under examination. Over complex terrain, the results of mesoscale numerical weather prediction model WRF can fulfill this need [18]. Wind resource assessment models with fine spatial resolution can solve the problem of optimal siting of turbines within a wind farm, while taking the interactions among the turbines and with complex terrains into account [19]. To achieve the same goal of increasing energy yields, measurements from several masts can be coupled using computational fluid dynamics [20]. The proposed models for evaluating a candidate site differ from the wind resource assessment models in the following aspects:  
* The available data cover a very limited time period. * The focus is on a single location. * There is a lack of time for measuring, while the wind resource assessment models are addressing the lack of measurement sites in spatial dimensions. Table 1 summarizes the requirements of candidate site evaluation, wind power forecasting, and wind resource assessment. The novel contribution of the presented work is the method how experimental modelling of wind speed time series is used to help with decisions on small wind turbine placement in complex terrain. To our knowledge, there have been no prior attempts to use a shorter measurement time series and compensate for it using modelling. The classical approach is collecting a long-term time series of wind measurements at the site and basing the decisions on these measurements. The drawback is the wait for the measurements to be collected.