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Renewable energy production has been surging in the United States and around the world in recent years. To mitigate increasing renewable generation uncertainty and intermittency, proactive demand response algorithms and programs are proposed and developed to improve the utilization of load flexibility further and increase power system operation efficiency. One of the biggest challenges to efficient control and operation of demand response resources is how to forecast the baseline electricity consumption and estimate the load impact from demand response resources accurately. In this paper, we propose to use a mixed-effect segmented regression model and a new robust estimate for forecasting the baseline electricity consumption in Southern California by combining the ideas of random effect regression model, segmented regression model, and the least trimmed squares estimate. Since the log-likelihood of the considered model is not differentiable at breakpoints, we propose a new backfitting algorithm to estimate the unknown parameters. The estimation performance and predictive power of the new estimation procedure have been demonstrated with both simulation studies and the real data application for the electric load baseline forecasting in Southern California.

A Robust Segmented Mixed Effect Regression Model for Baseline Electricity Consumption Forecasting