CONCLUSION

Two nonparametric models Root Transform Local Linear Regression and Kernel Density Estimation are proposed for estimating electric load PDF over the Gaussian distribution used in literature to improve the accuracy of electric load modeling. The performance of the nonparametric techniques was compared alongside the Gaussian and Gamma distribution and assessed using electric load data from over 2400 enterprise and residential locations in the United Kingdom using RMSE and the Kolmogorov-Smirnov test and data splitting. Root Transform Local Linear Regression had the best results across the board with the lowest Test RMSE values and with the most locations producing p-values greater than 0.01 when conducting the KS test followed by Kernel Density Estimation. The parametric distributions had overall the highest RMSE values per location and the KS test null hypothesis was rejected for all locations using those models. These results strongly suggest that RTLLR should be used in power systems planning and optimization studies for predicting stochastic load.