随着深度学习在电力负荷预测中的广泛应用，其非线性建模能力显著提升了预测精度。然而，深度学习技术在追求高精度的同时，面临模型参数量庞大的问题，导致计算效率低下，难以满足高实时性场景和资源受限环境的部署需求。针对上述问题，设计了一种基于自适应知识蒸馏的轻量化电力负荷预测模型，旨在实现预测精度与计算效率之间的优越平衡。首先，在教师模型构建阶段，设计了深度信息提取模块进而充分挖掘负荷数据中的深层特征，并结合长短期记忆网络构建高精度预测模型。在此基础上，进一步设计结构更为精简的学生模型，使其能够以少量参数完成负荷预测任务。为提升知识迁移效果，设计了一种基于学习方式的知识蒸馏方法，实现从教师模型到学生模型的高效知识传递。此外，在蒸馏过程中设计了自适应知识修正策略，进一步增强学生模型的学习能力。仿真实验结果表明，所提方法在进一步提升预测精度的同时，显著减少了模型参数（仅为原始模型的1.36%），有效实现了模型轻量化与整体性能的提升，验证了该方法在负荷预测任务中的有效性与优越性。

With the widespread application of deep learning in power load forecasting, its nonlinear modeling capability has significantly improved prediction accuracy. However, while pursuing high precision, deep learning models often suffer from a large number of parameters, resulting in low computational efficiency and difficulty in meeting the deployment requirements of high real-time scenarios and resource-constrained environments. To address these issues, this paper proposes a lightweight power load forecasting model based on adaptive knowledge distillation, aiming to achieve an optimal balance between prediction accuracy and computational efficiency. First, in the teacher model construction phase, a deep information extraction module is designed to fully explore the deep-level features of load data, combined with Long Short-Term Memory (LSTM) networks to build a high-precision forecasting model. On this basis, a more compact student model is further designed to perform load forecasting tasks with fewer parameters. To enhance the knowledge transfer effect, a learning-based knowledge distillation method is proposed to achieve efficient knowledge transfer from the teacher model to the student model. Additionally, an adaptive knowledge correction strategy is introduced during the distillation process to further improve the learning capability of the student model. Simulation results demonstrate that the proposed method not only further improves prediction accuracy but also significantly reduces model parameters (only 1.36% of the original model), effectively achieving model lightweighting and overall performance enhancement. These results validate the effectiveness and superiority of the proposed method in load forecasting tasks.