Short-term electricity load interval estimation algorithm based on OS-ELM feature enhancement for SVR

Abstract

Short-term power load forecasting is crucial for power system dispatch and market transactions. However, the dynamic, nonlinear, and uncertain nature of loads poses challenges for accurate interval estimation. This paper proposes a short-term load interval estimation algorithm based on online sequence extreme learning machine (OS-ELM) feature enhancement and support vector regression (SVR). Rather than a simple model stacking approach, it constructs a dynamically collaborative two-stage prediction architecture: OS-ELM serves as a real-time feature generator, adaptively capturing concept drift in data streams to produce dynamically enhanced features; these features are then fed into an SVR model for high-precision point prediction. Finally, dynamic confidence intervals are constructed based on the empirically updated prediction error distribution. Experiments validate the approach using actual summer data (26,304 samples) from a provincial power grid in China. Results demonstrate outstanding point prediction performance with an RMSE of 236.7 MW and MAPE of 1.80 %, outperforming benchmark models including SARIMA, LightGBM, LSTM, and Transformer. For interval forecasting, at a 95 % confidence level, it achieves 94.5 % interval coverage with an average interval width of 148.7 MW, striking a good balance between coverage and precision while maintaining high interval calibration quality. Furthermore, the scheduling scheme based on this algorithm strictly controls system voltage fluctuations within ±0.4 p.u., significantly enhancing system operational stability. This algorithm provides a solution for short-term load forecasting that combines high accuracy, strong adaptability, efficient computation, and reliable uncertainty quantification capabilities.