Distribution-level peak load prediction based on Bayesian Additive Regression Trees

Abstract

Accurate peak load prediction is an important element of the daily planning, operation, and dispatch scheduling in electric utilities. In order to build a better distribution-level peak load prediction reference model for the local utility in Albuquerque, a new nonparametric method, Bayesian Additive Regression Trees (BART), is introduced. First, a detailed analysis of peak load and local weather information from 2012 and 2013 are used. Strong linear relationship is displayed between the peak load and various weather factors during the winter and spring months and nonlinear relationship dominates in the summer and fall months. Next, the BART method is applied with a principled permutation-based inferential variable selection approach. The BART method's prediction accuracy is then compared with the Multiple Linear Regression (MLR), the result we developed when we cooperated with the local utility company, and the Support Vector Machine (SVM). After thoroughly analyzing and testing the methods based on different parameters, the methods are compared with Mean Square Error (MSE), Root Mean Square Error (RMSE), and Normalized Mean Square Error (NMSE) indexes. The BART displays the best prediction accuracy for every index in our case and its uncertain estimate further provides the confidence interval for the peak load prediction, which also has very high accuracy. Thus, the BART provides the local utility with a better reference peak load forecasting model. Last, influential weather and human factors are summarized.