An Ultra-Short-Term Probabilistic Load Forecasting Method Based on Joint Distribution Modeling

Ultra-short-term probabilistic load forecasting is of great significance for improving the safety and economy of the operation of new power systems. To address the problem that existing methods rarely focus on the dependence relationship between load forecast errors and the fluctuation characteristics of forecast output series, this paper proposes an ultra-short-term probabilistic load forecasting method based on joint distribution modeling. First, the Transformer model is used to perform high-precision point forecasting on historical load series, providing a reliable basis for subsequent uncertainty analysis. Second, based on an autoencoder, the forecast load series is reconstructed, and the reconstruction error is used as a deep fluctuation feature. The number of volatility patterns is determined through clustering-validity analysis, and the load scenarios are consequently divided into two representative fluctuation patterns, revealing the intrinsic relationship between the forecast-error distribution and load dynamic characteristics. Subsequently, for each pattern, a joint probability distribution model of load point forecast values and forecast errors is constructed separately; the Copula function is introduced to fit their dependence structure; the conditional error distribution is generated through inverse transformation; and finally, probabilistic prediction intervals are output according to confidence level requirements. Experiments on real-world load datasets show that the proposed method improves comprehensive accuracy metrics at different confidence levels.