Electrical Network Frequency (ENF) is a frequency of the electrical power signal of the power grid that plays a key role in the level of security. There is a difference in the values on the supply and demand on power usage. Due to its distinctive value, the ENF data hold great importance in the field of security. Examining the ENF signal makes it possible to trace the location where the ENF signal was generated. By making the most use of certain interpolation techniques, we can estimate the ENF value of a specific location and evaluate the estimated performance. Interpolating the ENF signals on the target location can increase the accuracy of the estimate for the unacquainted ENF signals. In this paper, we interpolated the ENF values of the power grid of the United States by using three different methods: IDW, Ordinary Kriging, and Universal Kriging. Then we evaluated the RMSE calculated by varying the hyper-parameters and models of interpolation methods. As a result, it was found that applying the Ordinary Kriging in the Western grid had the lowest RMSE. For the Eastern power grid, it was the IDW with λ = -1 which showed the lowest RMSE. We concluded that each power grid had different characteristics. Therefore different interpolation techniques should be applied to each case for precise approximation.