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张怀天等:基于         Transformer-集成学习的配电网短期负荷预测方法                            2026  年第 5 期



                  Construction, 2024, 45(12): 162–173.              LIANG  Rui,  JIN  Mohan,  ZHU  Huijun,  et  al.  Net  load  interval
               [23]   KONG W C, DONG Z Y, JIA Y W, et al. Short-term residential load  forecasting  in  rural  areas  based  on  multi-scenario  clustering  and
                  forecasting  based  on  LSTM  recurrent  neural  network[J].  IEEE  explainable temporal fusion transformers network[J]. Power System
                  Transactions on Smart Grid, 2019, 10(1): 841–851.  Technology, 2025, 49(12): 5019–5027, I0016–I0018.
               [24]   杨 国 华 ,  祁 鑫 ,  贾 睿 ,  等 .  基 于  CEEMD-SE  的  CNN  &  LSTM-  [30]   WANG C, WANG Y, DING Z T, et al. A transformer-based method
                  GRU  短期风电功率预测   [J]. 中国电力, 2024, 57(2): 55–61.    of multienergy load forecasting in integrated energy system[J]. IEEE
                  YANG  Guohua,  QI  Xin,  JIA  Rui,  et  al.  Short-term  wind  power  Transactions on Smart Grid, 2022, 13(4): 2703–2714.
                  forecast  based  on  CNN  &  LSTM-GRU  model  integrated  with  [31]   ZHAO H S, WU Y C, MA L B, et al. Spatial and temporal attention-
                  CEEMD-SE algorithm[J]. Electric Power, 2024, 57(2): 55–61.  enabled  transformer  network  for  multivariate  short-term  residential
               [25]   茹瑶, 赵永宁, 叶林, 等. 超短期  LSTM  风电功率预测模型的混合        load  forecasting[J].  IEEE  Transactions  on  Instrumentation  and
                  专家模块化代理解释方法      [J]. 电力建设, 2024, 45(11): 114–124.  Measurement, 2023, 72: 2524611.
                  RU  Yao,  ZHAO  Yongning,  YE  Lin,  et  al.  Modular  surrogate  [32]   俞 胜 ,  孙 可 ,  蔡 华 ,  等 .  结 合 极 端 梯 度 提 升 决 策 树 与 改 进
                  interpretation method based on decision tree mixture of experts for  Informer 的短期电力负荷预测方法  [J]. 中国电力, 2025, 58(10):
                  ultra-short-term  LSTM  wind  power  forecasting  model[J].  Electric
                                                                    195–205.
                  Power Construction, 2024, 45(11): 114–124.
                                                                    YU  Sheng,  SUN  Ke,  CAI  Hua,  et  al.  A  short-term  power  load
               [26]   黄文琦, 梁凌宇, 王鑫, 等. 基于变量选择与  Transformer 模型的中
                                                                    forecasting  method  combining  extreme  gradient  boosting  decision
                  长期电力负荷预测方法      [J]. 浙江大学学报  (理学版), 2024, 51(4):
                                                                    tree  with  an  improved  informer[J].  Electric  Power,  2025,  58(10):
                  483–491, 500.
                                                                    195–205.
                  HUANG Wenqi, LIANG Lingyu, WANG Xin, et al. Mid-long term
                                                                 [33]   孟浩, 徐飞, 符帅, 等. 考虑温控型负荷特性影响的集群用户超短期
                  power  load  forecasting  based  variable  selection  and  transformer
                                                                    负荷预测方法    [J]. 中国电力, 2025, 58(12): 63–72, 85.
                  model[J].  Journal  of  Zhejiang  University  (Science  Edition),  2024,
                                                                    MENG  Hao,  XU  Fei,  FU  Shuai,  et  al.  Ultra-short-term  load
                  51(4): 483–491, 500.
                                                                    forecasting  method  for  aggregated  users  considering  the  impact  of
               [27]   孟衡, 张涛, 王金, 等. 基于多尺度时空图卷积网络与      Trans-
                                                                    temperature-controlled load characteristics[J]. Electric Power, 2025,
                  former 融合的多节点短期电力负荷预测方法       [J]. 电网技术, 2024,
                                                                    58(12): 63–72, 85.
                  48(10): 4297–4305.
                                                                 [34]   Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way
                  MENG Heng, ZHANG Tao, WANG Jin, et al. Multi-node short-term
                                                                    to  prevent  neural  networks  from  overfitting[J].  The  journal  of
                  power load forecasting method based on multi-scale spatiotemporal
                                                                    machine learning research, 2014, 15(1): 1929–1958.
                  graph  convolution  network  and  transformer[J].  Power  System
                                                                 [35]   Cheng  B,  Chen  Y.  Open  Datasets  for  grid  modeling  and
                  Technology, 2024, 48(10): 4297–4305.
                                                                    visualization:  an  Alberta  power  network  case[J].  arXiv  preprint
               [28]   范士雄, 李东琦, 郭剑波, 等. 基于时变滤波经验模态分解-重构和
                                                                    arXiv: 2504, 07870: 2025.
                  独立自注意力机制的     iTransformer 超短期负荷预测方法  [J]. 电网

                  技术, 2025, 49(6): 2436–2445.                                   作者简介:
                  FAN Shixiong, LI Dongqi, GUO Jianbo, et al. Ultra-short-term load    张怀天(1994),女,硕士,工
                  forecasting  method  based  on  time-varying  filter  empirical  mode  程师,从事配电网运行控制研究,E-mail:
                  decomposition-reconstruction  and  iTransformer  with  stand-alone  huaitianz@163.com。
                  self-attention mechanism[J]. Power System Technology, 2025, 49(6):
                  2436–2445.
               [29]   梁睿, 金沫含, 朱慧君, 等. 基于多场景聚类和可解释时间融合
                  Transformer 网络的乡村地区净负荷区间预测    [J]. 电网技术, 2025,
                                                                                            (责任编辑 王文诗)
                  49(12): 5019–5027, I0016–I0018.





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