针对电力系统碳排放峰值预测中数据质量低、模型超参数耦合度高及峰值定位误差大等现实问题，提出了一种融合鲁棒数据预处理与改进灰色-卷积混合模型的预测框架。首先，建立基于动态分位数边界的离群值检测与多窗口加权鲁棒修复流程，并引入基于随机森林特征重要性的链式多重插值法，以解决非高斯分布数据中的离群干扰和高维缺失问题；在此基础上，构建融合变分模态分解、空洞卷积与注意力门控的改进卷积网络，嵌入改进灰色模型提取长期趋势，并通过灰色关联度引导的鲸鱼优化算法完成超参数寻优。实验结果表明：与遗传算法、粒子群优化及灰狼优化算法相比，所提算法将平均绝对百分比误差分别降低 39.7%、32.5%、25.4%，峰值预测时刻偏差分别降低 77.1%、71.4%、60.0%；与自回归整合移动平均 (autoregressive integrated moving average, ARIMA)、长短期记忆网络-先知混合模型 (long short-term memory-prophet, LSTM-Prophet)、时序变换器(time-series transformer，TST)、经验模态分解-长短期记忆网络 (empirical mode decomposition - LSTM，EMDELSTM)、变分模态分解-门控循环单元(variational mode decomposition-gated recurrent unit, VMD-GRU) 相比，所提模型将平均绝对百分比误差降至2.89%，峰值预测时刻偏差降至 0.7 h，拐点捕捉率提升至 93.8%。该研究为碳排放峰值精准预测提供了新的技术路径，并为电力系统减排策略制定提供了数据支持。

To address the practical challenges of poor data quality, strong hyperparameter coupling, and significant peak positioning  errors  in  power  system  carbon  emission  peak  forecasting,  a  framework  integrating  robust  data  preprocessing with an improved grey-convolution hybrid model is proposed. Firstly, a dynamic quantile boundary-based outlier detection and multi-window weighted robust repair procedure is established, together with a random forest feature importance-based chained  multiple  imputation  method,  to  suppress  outlier  disturbances  and  high-dimensional  missingness  in  non-Gaussian data. Subsequently, an improved convolutional network incorporating variational mode decomposition, dilated convolution,and attention gating is constructed, with an embedded improved grey model for long-term trend extraction; hyperparameter optimization is achieved through a grey relational grade-guided whale optimization algorithm. Experimental results show that  compared  with  genetic  algorithm,  particle  swarm  optimization,  and  grey  wolf  optimization,  the  proposed  algorithm reduces mean absolute percentage error by 39.7%, 32.5%, and 25.4%, and peak prediction time deviation by 77.1%, 71.4%,and 60.0%, respectively. Compared with autoregressive integrated moving average (ARIMA), long short-term memory -prophet  (LSTM-Prophet),  time-series  transformer  (TST),  empirical  mode  decomposition-LSTM  (EMDE-LSTM),  and variational mode decomposition - gated recurrent unit (VMD-GRU), the proposed model reduces mean absolute percentage error to 2.89% and peak prediction time deviation to 0.7 h, while improving the inflection point capture rate to 93.8%. This study provides a new technical approach for accurate carbon emission peak prediction and offers data support for power system emission reduction strategy formulation.