针对省级区域碳排放预测由于历史数据不足导致预测精度较低问题，提出了一种基于电力数据的省级区域碳排放预测方法。首先，分析了省级区域电力负荷数据与碳排放总量之间的相关性；然后，构建长短期记忆-梯度提升回归树（long short-term memory-gradient boosting regression tree，LSTM-GBRT）混合模型，并基于电-碳相关性完成省级区域碳排放预测；为进一步提升预测精度，采用K-means聚类算法对各省级区域能源消费结构进行聚类分析，挖掘碳排放特征相似省份，用于完善数据集与特征集；最后，基于我国各省级区域的电力和碳排放历史数据对某省碳排放进行预测，结果表明，该模型能够有效地预测碳排放趋势，平均绝对百分误差、平均绝对误差和均方根误差指标均优于LSTM、卷积神经网络（convolutional neural network，CNN）和门控循环单元（gated recurrent unit，GRU）等模型，展示出更好的泛化能力与鲁棒性。

To address the issue of low prediction accuracy in provincial-level area carbon emission forecasting caused by insufficient historical data, a novel prediction method based on electricity data is proposed. Firstly, the correlation between provincial-level area power load data and total carbon emissions is analyzed. Subsequently, a hybrid long short-term memory⁃gradient boosting regression tree (LSTM-GBRT) model is constructed to perform carbon emission forecasting, based on the electricity-carbon correlation. To further enhance prediction accuracy, a K-means clustering algorithm is employed to classify provincial-level area energy consumption structures, enabling the identification of provinces with similar carbon emission characteristics. This clustering aids in enriching both the dataset and feature set. Finally, based on historical electricity and carbon emission data across multiple provincial-level area in China, the model is applied to forecast emissions in a target province. The results demonstrate that the proposed model can effectively capture the carbon emission trend. It consistently outperforms benchmark models-including long short-term memory (LSTM), convolutional neural network (CNN), and gated recurrent unit (GRU)-in terms of mean absolute percentage error, mean absolute error, and root mean square error. These findings highlight the model’s superior generalization performance and robustness across varying data scenarios.