Abstract: Electric load forecasting is a critical component of power grid optimization and scheduling. However, existing purely data-driven methods and strategies that incorporate domain knowledge often struggle to accurately capture long-term trends and periodic patterns in the context of complex dynamic environments and non-stationary load characteristics, thereby affecting forecasting accuracy and robustness. To address these challenges, this paper proposes a power load forecasting model based on the integration of domain prior knowledge, named DPK-ELF. The model utilizes a prior knowledge extraction module to deeply analyze the dynamic behavior characteristics of time series data, constructing domain-specific prior knowledge tailored to the data. It employs a dynamic segmented stacked moving average smoothing method to extract prior trends in power loads. Subsequently, the prior trend decomposition module decomposes the power load series into prior smoothed trends and residual local stochastic fluctuations, which are then predicted using the PatchTST data-driven model. Additionally, during model training, a soft constraint optimization technique is applied, treating domain prior knowledge as boundary constraints within the loss function to enhance model robustness. Validation on four public power load datasets demonstrates that DPK-ELF significantly outperforms advanced baseline models such as PatchTST, DLinear, Autoformer, and Informer in three key performance metrics: MSE, MAE, and RSE. Specifically, compared to the PatchTST model, DPK-ELF achieves improvements of up to 28.31% in MSE, 19.57% in MAE, and 14.94% in RSE on the Australian electricity price and load dataset; and 12.25% in MSE, 7.77% in MAE, and 6.29% in RSE on the PDB power demand dataset. These results strongly demonstrate the significant advantages of the DPK-ELF model in improving forecasting accuracy.

摘要： 电力负荷预测是电网优化调度的重要环节，但面对复杂的动态环境和非完全平稳的负荷特性，现有纯数据驱动方法和结合领域知识的策略仍存在对长期趋势和周期性规律捕捉不足的问题，影响预测精度和鲁棒性。为此，本文提出了一种基于领域先验知识融合的电力负荷预测模型（DPK-ELF）。该模型通过先验知识抽取模块，深入分析时间序列数据的动态行为特征，构建针对具体数据的领域先验知识，并利用动态分段堆叠平均平滑法提取电力负荷的先验趋势。接着，先验趋势分解模块将电力负荷序列分解为先验平滑趋势和残差局部随机波动，再结合PatchTST数据驱动模型进行预测。同时，在模型训练阶段采用软约束优化技术，将领域先验知识作为损失函数的边界约束，提升模型的鲁棒性。在四个公开电力负荷数据集上的验证结果表明，等先进对比模型。其中，与PatchTST模型相比，DPK-ELF在澳大利亚电价与电力负荷数据集上，MSE提升高达28.31%，MAE提升19.57%，RSE提升14.94%；在PDB电力需求数据集上，MSE提升12.25%，MAE提升7.77%，RSE提升6.29%。这些结果充分证明了DPK-ELF模型在提升预测精度方面的显著优势。