Prediction of Power System Ramping Demand Using Meteorological Features

Power system ramping demand consists of deterministic and uncertain components. This study focuses on predicting uncertain ramping demand influenced by meteorological factors. First, an adaptive trend identification algorithm is proposed to extract key features of upward and downward ramps, including magnitude and duration. Multidimensional correlation analysis quantifies the relationships among net load characteristics, historical load forecasts, weather predictions, historical prediction errors, and uncertain ramping demands. The minimum redundancy maximum relevance (mRMR) method is applied to compress input data features, and a TimeXer-modified prediction framework is developed to capture long-term dependencies in time-series data while integrating meteorological features through attention mechanisms, outputting confidence intervals for uncertain ramping demand. To enhance performance under extreme weather conditions, a method for defining extreme weather indicators is proposed, establishing thresholds based on historical meteorological data distributions and creating binary features for each time step to denote extreme weather. The original TimeXer model’s output layer is modified to predict both the mean and variance of ramping demand, addressing uncertainty in extreme weather scenarios. A Gaussian negative log-likelihood loss function is employed for training to optimize uncertainty prediction performance. Validation using 2024 and 2025 operational data from Shandong, China, indicates that the proposed model improves prediction accuracy by 18.4%, achieves a 92.5% interval coverage rate, and produces narrower prediction interval bandwidths under equivalent coverage rates.