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, 影响了预测精度和鲁棒性。为此, 提出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。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, 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4个公开电力负荷数据集上的实验结果表明, DPK-ELF在均方误差(MSE)、平均绝对误差(MAE)和相对平方误差(RSE) 3个关键性能指标上均优于PatchTST、DLinear、Autoformer、Informer等对比模型。在澳大利亚电价与电力负荷数据集上, 与PatchTST模型相比, DPK-ELF的MSE、MAE、RSE分别提升28.31%、19.57%、14.94%;在PDB电力需求数据集上, 与PatchTST模型相比, DPK-ELF的MSE、MAE、RSE分别提升12.25%、7.77%、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。

关键词: 电力负荷预测, 领域先验知识, 趋势分解, PatchTST模型, 软约束优化

Abstract:

Electric power load forecasting is a crucial step in optimal dispatching of power grids. However, when confronted with complex dynamic environments and incompletely stationary load characteristics, existing purely data-driven methods and strategies incorporating domain knowledge insufficiently capture long-term trends and periodic patterns, which affects forecasting accuracy and robustness. To address this issue, an electric power load forecasting model based on the integration of domain prior knowledge, named DPK-ELF, is proposed. This model utilizes a prior knowledge extraction module to thoroughly analyze the dynamic behavioral characteristics of time-series data, constructs domain prior knowledge specific to the given data, and employs a dynamic piecewise stacking average smoothing method to extract prior trends in electric power loads. The prior-trend decomposition module decomposes the electric power load series into prior smoothed trends and residual local random fluctuations, which are then combined with the PatchTST data-driven model for forecasting. Soft constraint optimization techniques are adopted during the model training phase using domain prior knowledge as boundary constraints in the loss function to enhance the robustness of the model. Experiments on four publicly available electric power load datasets reveal that DPK-ELF outperforms comparison models such as PatchTST, DLinear, Autoformer, and Informer across three key performance metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), and Relative Squared Error (RSE). In the Australian electricity price and power load dataset, compared with the PatchTST model, DPK-ELF achieves improvements of 28.31%, 19.57%, and 14.94% in MSE, MAE, and RSE, respectively. In the PDB power demand dataset, compared with the PatchTST model, DPK-ELF achieves improvements of 12.25%, 7.77%, and 6.29% in MSE, MAE, and RSE, respectively. These results demonstrate the significant advantages of the DPK-ELF model in improving forecasting accuracy.

Key words: electric power load forecasting, domain prior knowledge, trend decomposition, PatchTST model, soft constraint optimization