Electric Load Probability Density Estimation using Root-Transformed Local Linear Regression

Probability density estimation of stochastic electric load is of importance nowadays in power system operations and urban planning due to the uncertainties in network demand that affects the operating states of power systems. This in turn requires accurate and reliable methods to estimate network loads, especially in distribution networks. This paper proposes employing the root-unroot method in combination with local linear regression for estimating electric load probability density. Using measured load data obtained for a range of commercial enterprises, the performance of the proposed model is compared with two kernel density estimation models and two traditional parametric models (Gaussian and Gamma) and is assessed using a variety of error metrics and statistical tests. Results confirm the accuracy of the nonparametric models over the parametric models with the root transform model performing the best across all error metrics and K-S goodness-of-fit test.