Scenarios Generation-based Multiple Interval Prediction Method for Electricity Prices

This paper introduces an innovative interval prediction methodology aimed at addressing the limitations of current evaluation indicators while enhancing prediction accuracy and reliability. To achieve this, new evaluation metrics are proposed, offering a comprehensive assessment of interval prediction methods across both all-sample and single-sample scenarios. Additionally, a novel Pattern-Diversity Conditional Time-Series Generative Adversarial Network (PDCTSGAN) is developed, designed to generate realistic scenarios and support a new interval prediction framework based on scenario generation. The PDCTSGAN model incorporates unique modifications to random noise inputs, enabling the creation of pattern-diverse and realistic scenarios. These scenarios are then utilized to produce multiple interval patterns characterized by high coverage probability and reduced average width. The proposed approach is validated through detailed case studies, and the paper concludes with a discussion of future research directions to further refine interval prediction techniques.