## 5 Conclusions  
This paper compared the power production results of four different methods/models against measurements for the offshore wind farm of Lillgrund, which is in Sweden. Two ACD methods based on the 1D momentum theory are compared along with two analytical wake models, the Jensen and the Larsen. For this comparison two main wind directions are investigated. The main conclusions are: (i) The ACD (2016) method and the Larsen model outperform the other method and model in most cases. (ii) The power increase of the turbine after the gap is better captured when using the ACD (2016) method. (iii) The results from the ACD (2016) method show a clear improvement in the estimated power production in comparison to the ACD (2008) method. (iv) The Jensen method seems to overestimate the power deficit for all cases. (iv) The ACD (2016) method, despite its simplicity, can capture the power production within the error margin although it tends to underestimate the power deficit. One may say that the ACD (2016) method in RANS, which has much lower computational requirements than the ACD method in LES at the cost of lower accuracy, could represent a good compromise.  
It should be noted that the ACD (2008) approach uses a simplified approach to calculate the power production of the wind turbine, that is the wind velocity at hub height. The ACD methods presented in [20,49] on the other hand use an average of the wind speed over the rotor. As such, it would be interesting for a future study to include a more advanced ACD method in a comparison study with the ACD (2016).  
Additionally, [44] showed that by using the \(k-\varepsilon\)\(f_{p}\) turbulence model the power production results for the second wind turbine in the row are in closer agreement with the measurements than when using the \(k-\varepsilon\) model. Hence future work could focus on researching the impact of using different turbulence closure models on the results. Also, as the Lillgrund wind farm is quite unique due to its very close inter-row spacing of the wind turbines, it would be advantageous to apply a similar comparative analysis using these methods/models on wind farms with larger distances between wind turbines. Furthermore, as stability has an impact on wake development it is important to explore wind farm cases in which the data can be filtered for stability. Finally, it would prove beneficial to continue investigating the offshore Lillgrund wind farm for other wind flow conditions to obtain a more comprehensive understanding of the performance of the methods/models.  
N.S.: Conceptualization, Formal analysis, Methodology, Software, Validation, Writing--original draft, Writing--review & editing, Corresponding author. H.P.: Conceptualization, Supervision, Visualization, Writing--original draft, Writing--review & editing, S.I.: Funding acquisition, Project administration, Resources, Supervision. All authors have read and approved the final version of the paper.  
This research was funded by the Research Council of Norway (Project no. 231831).  
The filtration and validation of the data was performed by [PERSON] (DTU Institute for Vindenergy Denmark) according to the guidelines described in [50]. [PERSON] is kindly acknowledged for proof reading the manuscript.