Annual Load Scenario Generation Using a Hybrid STL and Improved DDPM Approach

To address the limitations of existing annual load scenario generation methods, including insufficient ability to represent long-term trends, excessive randomness in generated scenarios, and inadequate consideration of special holiday conditions, in this paper, an annual load curve generation method is proposed that integrates Seasonal–Trend decomposition using Loess (STL) with an improved denoising diffusion probabilistic model (DDPM). In the proposed method, the STL algorithm is first applied to decompose the annual load curve into a trend component and a daily seasonal component. The trend component is used as a baseline to ensure that the generated load curves remain consistent with the actual long-term trend characteristics. On this basis, an improved diffusion-based denoising model is employed to achieve controllable generation of different types of daily load scenarios. Finally, the generated daily load scenarios are aggregated with the trend component on an hourly basis to construct annual load scenario curves that simultaneously preserve realistic trend behavior and stochastic fluctuations. A case study based on a city in China is used to evaluate the proposed method. The results demonstrate that both the generated daily load scenarios and annual load scenarios outperform existing benchmark methods across multiple quantitative evaluation metrics, thereby validating the effectiveness of the proposed load scenario generation approach.