南方电网技术2026，Vol.20Issue(8)：67-77,11.DOI:10.13648/j.cnki.issn1674-0629.2026.08.006
基于深度学习的乡村综合能源系统短期负荷预测
Short-Term Load Forecasting for Rural Integrated Energy Systems Based on Deep Learning
摘要
Abstract
Accurate short-term load forecasting of rural integrated energy systems is of great significance for the safe and stable opera-tion 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.关键词
乡村综合能源系统/负荷预测/Informer模型/双向门控循环单元(BiGRU)/变分模态分解(VMD)Key words
rural integrated energy system/load forecasting/Informer model/bidirectional gated recurrent unit(BiGRU)/variational mode decomposition(VMD)分类
信息技术与安全科学引用本文复制引用
杨浚文,徐志,姜訸,覃日升,任敏,赵男,袁志伟,李海良..基于深度学习的乡村综合能源系统短期负荷预测[J].南方电网技术,2026,20(8):67-77,11.基金项目
智能电网重大专项(2030)资助项目(2024ZD0800600).Supported by the National Science and Technology Major Project for Smart Grid(2030)(2024ZD0800600). （2030）
