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This study aimed to apply empirical data to assess wind energy production at the Búrfell site in Iceland based on the E44 turbine model. The empirical data are 5 years of recordings at the site location by the Iceland Metrological office. The wind speed data are measured at a 10 m height from 2017 to 2021. There are two E44 wind turbines test installed on the site. In the previous studies, the wind farm capacity and Levelized cost of energy (LCOE) were reported without investigating the wake loss model and its impacts on LCOE and have an estimation applied. The previous research was based on the two installed wind turbines at the site, which are located in a straight line and perpendicular to the prevailing wind speed. This study applies the Jensen-Katic model to investigate wake loss. Downwind and crosswind ten-rotor diameters and five-rotor diameters are calculated respectively as the best options. Afterward, an appropriate number of wind turbines is suggested for 80MW production. In addition, this study's optimum capacity factor (CF) is 26.08%, which was reported at 37.9% - 38.38% before. On average, the turbines produce less than 30% of their rated power, which has been reported at 38.15% in prior studies. This study presents the LCOE as equal to 0.0659 USD/kWh, which is less than 0.0703 USD/kWh in the previous studies and the LCOE reported by the 2020 LCOE European report. The obtained LCOE in this study is based on the weighted average cost of capital in the energy project by Landsvirkjun, the national power company of Iceland. The obtained result from the model used, which matched the empirical measurements, displays Iceland's best rank for wind energy LCOE metric among European countries. The proposed method provides a vision to use the wake loss model output in deep learning training to predict power production, leading to a sustainable and reliable power grid., This work was performed in the Center of Excellence (CoE) Research on AI and Simulation-Based Engineering at Exascale (RAISE), the EuroCC and the EuroCC 2 projects receiving funding from EU’s Horizon 2020 Research and Innovation Framework Programme under grant agreement no. 951733, no. 951740 and no. 101101903, respectively.