Ensemble-based peak demand probability density forecasting with application to risk-aware power system scheduling

Power systems face increasing challenges in maintaining resource adequacy due to lower operating margins, rising renewable energy uncertainty, and demand variability. Forecasting the probability distribution of peak demand on shorter timescales is a critical forward-facing issue under increasing volatility. This study introduces a novel ensemble-based machine learning method for peak demand probability density forecasting that extends classical extreme value theory to model time series peaks as nonstationary statistical distributions. The approach employs an ensemble of tree-based learners that recursively partition the covariate space and estimate local generalized extreme value distributions, allowing it to automatically capture complex covariate-dependent parameter variations. Unlike existing approaches, which often suffer from convergence issues or restrictive functional forms, this framework is both flexible and robust. Validation on a case study based on the PJM interconnection demonstrates that the method achieves a 38 percent reduction in committed capacity when generation is scheduled based on a reliability criterion. These improvements provide practical value for power system operation, enabling risk-aware capacity scheduling under peak demand uncertainty and supporting reliability-driven decision making in future energy systems. • Combined machine learning with extreme value analysis for flexible peak density modeling. • Formulated a conditional probability density model for time series peaks. • Introduced a nonstationary extreme density estimator that robustly models parameter variations. • Enabled short-term risk-sensitive scheduling and reliability assessment. • Achieved a 38% reduction in committed capacity in the PJM interconnection case study.