## 1 Introduction  
Small wind turbines are a useful technology for transitioning to a more sustainable energy model. They are a good way to decentralize electricity generation, an objective set by the European Union through its 2009/72/EC directive [1]. Among these, Building Integrated Wind Turbines (BIWT) are specially interesting for many reasons, such as:  
* Since they are installed on buildings and can be installed within cities, which are both big energy consumers, transport and transformation losses are reduced. * Being installed on already urbanized areas, they avoid some of the environmental [2] and social [3] impacts of conventional wind farms, such as land use, visual pollution of natural areas, and destruction or disruption of habitats. * They can be installed alongside solar panels to create hybrid generation systems. Power generation becomes more stable compared to pure photovoltaic system of the same rated power. In isolated installations, this can lead to a lower set-up cost because less batteries are needed to ensure that demand is met [4]. * They can play a key role on the so called nearly Zero-Energy Buildings [5]. With the promotion of small wind turbines in mind, the authors of this paper have been working on a novel Savonious-type small wind turbine specifically designed for urban environments calledROSEO-BIWT, which has already been presented in previous papers [6; 7]. We have the conviction that any small wind turbine has to be cost effective if it is going to contribute to the mentioned energy transition, and a proper wind resource assessment method is key for that [8]. Traditional wind farms require extensive monitoring and measurements plans. For instance, according to the National Renewable Energy Lab [9], a single measurement tower that takes wind and other variable readings costs about US$25,000 and US$40,000 to set up and maintain. A small wind turbine does not justify such an expensive and detailed analysis to spot a good location, or to calculate the potential of an already chosen location. Apart from that, the topography of the place has to be taken into account. [PERSON] et al. propose using CFD (Computational Fluid Dynamics) simulation for each site for this, but, as they note, it consumes a large amount of time and computing capacity [10]. The aim of this paper was to present a wind resource estimation method that requires few computational resources and that takes into account the particularities of our turbine, primarily being its high sensitivity to the wind direction and the concentrating effect of the building and the concentration panels. Furthermore, each aspect of this method can be applied to other small wind turbine designs. It estimates the energy production of a previously selected building, allowing for calculation of the return on investment. It also helps selecting the facade or facades most suitable for an installation. For this study, the specific case of the University of the Basque Country's building in the town of Eibar was studied [11]. There have already been attempts to develop efficient methods for small wind turbines. [PERSON] et al. [12] suggests fitting linear functions that given the wind module speed of a reference site output the estimated module of a target site. This method has some inconveniences. First of all, it can predict negative wind speed values at the target side because of the residual scatter term. The authors solved this by setting the negative predicted values to the mean of the function before applying that term. However, this could lead to overestimating the wind potential at the target site. The second inconvenient is that a linear regression escalates the whole wind speed range with the same factor: the slope of the linear function. This means that non-linear effects cannot be properly taken into account. Instead of this, we propose using quantile mapping to come with a similar method that does not have such inconveniences and that is specially suitable to get the overall potential of a site.