摘要： 针对高渗透率分布式能源接入下微电网负荷序列的强非线性、非平稳特性，以及现有预测模型因全局建模平滑效应导致的局部突变区精度流失、极值点预测失真等核心问题，提出一种基于误差感知机制的多尺度特征短期负荷预测模型（MAG-TL）。现有单一深度学习模型难以兼顾长程周期规律与局部瞬态波动的多尺度特征提取；组合预测模型普遍存在分解参数依赖人工经验、特征融合方式粗糙且缺乏高波动区域误差修正机制，其中长程建模能力突出的Transformer类模型在负荷剧烈跳变时因加权聚合特性产生平滑效应，尖峰与低谷预测偏差显著，难以满足微电网精细化调度需求。该模型构建了多尺度特征解构、全局依赖建模、靶向误差修正、全局残差补全的四级协同预测架构。首先采用改进鲸鱼优化算法（IWOA），以包络熵最小化为目标，通过自适应惯性权重机制平衡全局寻优与局部收敛能力，自适应确定变分模态分解的最优参数，将原始非平稳负荷序列精准解耦为多个具有明确物理尺度的固有模态分量，结合筛选后的气象关联特征构建多尺度输入特征集；其次通过并行一维卷积神经网络提取各模态深层特征，利用自适应注意力模块动态分配各模态特征权重并进行多尺度自适应融合，在强化关键误差特征的同时保留全局基准信息；随后将融合特征输入Transformer编码器捕捉全局长时序依赖，内置轻量化误差感知投影层，基于隐层特征的空间偏移程度量化各时间步的预测误差风险，采用Top-K相对排序机制动态筛选高误差锚点，无需真实标签介入即可自主定位负荷突变区域；最后构建误差感知增强型长短期记忆网络解码器，通过残差融合机制整合锚点特征、局部误差评分与全局误差背景特征，针对锚点区域生成乘性增益完成幅值靶向修正，设置动态修正阈值避免平稳时段过度补偿，同时引入轻量化全连接残差网络生成全局加性补偿量，形成乘性修正突变幅值、加性补全系统偏差的分级修正机制。基于贵阳地区含高渗透率新能源的微电网18个月实测运行数据开展验证，训练集、验证集、测试集按7:2:1划分，与3种单一模型、4种组合模型进行多维度性能对比。结果表明，在贵阳数据集实验中所提模型均方根误差为10.71MW，平均绝对误差为4.78MW，平均绝对百分比误差为0.77%，决定系数达0.992；与性能最优的对比组合模型相比，均方根误差降低12.23MW，与单一预测模型相比最大降低34.81MW。消融实验验证了各核心模块的独立增益，误差感知机制使均方根误差降低5.71MW；误差感知分数与实际预测残差的皮尔逊相关系数达0.8071，风险量化机制具备高精准度。模型单样本推理延迟为2.34ms，可充分满足微电网在线闭环调度的实时性要求。该模型有效缓解了全局建模固有的平滑效应瓶颈，以可控计算开销实现预测精度有效提升，能够精准捕捉负荷剧烈波动误差的瞬态变化特征，可为高渗透率新能源场景下的微电网精准调度提供可靠的数据支撑与技术参考。

Abstract: Aiming at the strong nonlinearity and non-stationarity of microgrid load series under high penetration of distributed energy resources, as well as core drawbacks of existing forecasting models including accuracy degradation in abrupt load regions and distorted extreme value prediction caused by the smoothing effect of global modeling, this paper proposes a multi-scale feature short-term load forecasting model with an error-aware mechanism (MAG-TL). Existing standalone deep learning models cannot simultaneously extract multi-scale features covering long-term periodic patterns and local transient fluctuations; hybrid forecasting models generally rely on manually tuned decomposition parameters, adopt simplistic feature fusion, and lack error correction mechanisms for high-volatility segments, and notably, Transformer-based models with outstanding long-term sequence modeling capability exhibit a smoothing effect due to weighted feature aggregation under drastic load fluctuations, resulting in notable peak and valley prediction errors and failing to support refined microgrid dispatch. The model establishes a four-stage collaborative forecasting pipeline of multi-scale feature decomposition, global dependency modeling, targeted error correction and global residual compensation: first, an improved whale optimization algorithm (IWOA) with adaptive inertia weight is employed to minimize envelope entropy, balancing global exploration and local exploitation to adaptively optimize variational mode decomposition (VMD) parameters, so that the original non-stationary load sequence is decoupled into physically interpretable intrinsic mode functions (IMFs), and a multi-scale input feature set is constructed with selected meteorological correlation features; second, parallel 1D convolutional neural networks extract deep features from each IMF, and an adaptive attention module dynamically assigns modal feature weights for adaptive fusion, enhancing error-sensitive features while preserving global baseline information; third, the fused features are fed into a Transformer encoder to capture long-range temporal dependencies, where a lightweight error-aware projection layer quantifies per-step prediction risk via hidden feature spatial offset, and a Top-K relative ranking mechanism dynamically screens high-error anchor points to autonomously locate load mutation regions without ground-truth labels; finally, an error-aware enhanced LSTM decoder integrates anchor features, local error scores and global error background via residual fusion, generates multiplicative gains for targeted amplitude correction in anchor regions with a dynamic threshold to avoid over-compensation in stationary periods, and a lightweight fully connected residual network is introduced to output global additive compensation, forming a hierarchical correction mechanism of multiplicative mutation amplitude correction and additive systematic bias compensation. Verification is conducted on 18-month field data of a high-renewable-penetration microgrid in Guiyang, with the dataset split into training, validation and test sets at a 7:2:1 ratio, and multi-dimensional performance comparisons are carried out against 3 standalone models and 4 hybrid models. The results show that on the Guiyang dataset, the proposed model achieves a root mean square error (RMSE) of 10.71 MW, a mean absolute error (MAE) of 4.78 MW, a mean absolute percentage error (MAPE) of 0.77%, and a coefficient of determination (R²) of 0.992; compared with the best-performing hybrid baseline model, its RMSE is reduced by 12.23 MW, and the maximum RMSE reduction reaches 34.81 MW compared with standalone forecasting models. Ablation experiments verify the independent contribution of each core module, and the error-aware mechanism alone reduces RMSE by 5.71 MW; the Pearson correlation coefficient between error-aware scores and actual prediction residuals reaches 0.8071, confirming the high precision of the risk quantification mechanism. The single-sample inference latency of the model is 2.34 ms, which can fully meet the real-time requirement of microgrid online closed-loop dispatch. The proposed model effectively alleviates the inherent smoothing bottleneck of global modeling, improves forecasting accuracy with controllable computational overhead, and can accurately capture the transient variation characteristics of load fluctuation errors, providing reliable data support and technical reference for precise microgrid dispatch under high renewable penetration scenarios.