乡村综合能源系统短期负荷的准确预测对于乡村电网的安全稳定运行及新能源就地消纳具有重要意义。然而，受农业生产节律和天气突变等多重因素耦合影响，乡村综合能源系统负荷呈现出强随机性、非平稳性及多时间尺度特征，导致传统方法预测精度受限。为此提出了一种基于VMD-Informer-BiGRU模型的短期负荷预测方法。该方法首先构建了包含农忙时段、环境温度、节假日等多维特征集，以精准表征乡村特有的用能模式；利用变分模态分解（variational mode decomposition，VMD）将原始负荷序列解耦为具有不同频率特性的模态分量，有效消除随机波动干扰；在此基础上引入Informer模型深度挖掘全局长程时间依赖，并协同双向门控循环单元（BiGRU）捕捉局部突变特征，实现多尺度时序特征的深度融合；最后构建了组合预测模型。以中国某乡村地区实际用能场景为预测对象，计算结果表明所提模型的预测精度高于其他模型。

Accurate short-term load forecasting of rural integrated energy systems is of great significance for the safe and stable operation of rural power grids and local renewable energy consumption. However, due to the coupled effects of agricultural production rhythms, sudden weather changes, and other factors, the load of rural integrated energy systems exhibits strong randomness, non-stationarity, and multi-time-scale characteristics, which limit the forecasting accuracy of traditional methods. To address this issue, a short-term load forecasting method is proposed based on the VMD-Informer-BiGRU model. Firstly, a multidimensional feature set including farming periods, ambient temperature, and holidays is constructed to accurately characterize the unique energy consumption patterns in rural areas. Variational mode decomposition (VMD) is then used to decompose the original load sequence into modal components with different frequency characteristics, effectively reducing random fluctuation interference. On this basis, the Informer model is introduced to extract global long-term temporal dependencies, while the bidirectional gated recurrent unit (BiGRU) is employed to capture local mutation features, achieving deep fusion of multi-scale temporal features. Finally, a combined forecasting model is established. Using an actual rural energy consumption scenario in China as the case study, the results show that the proposed model achieves higher forecasting accuracy than other models.