| 引用本文: | 张 恒,郑建勇,梅 飞,等.基于VMD和辅助任务学习的短期负荷预测方法[J].电力系统保护与控制,2025,53(05):104-112.[点击复制] |
| ZHANG Heng,ZHENG Jianyong,MEI Fei,et al.Short-term load forecasting method based on VMD and auxiliary task learning[J].Power System Protection and Control,2025,53(05):104-112[点击复制] |
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| 摘要: |
| 日高峰时段负荷的强波动性和随机性极大地影响了传统方法在进行负荷预测时的准确性，提出一种基于变分模态分解(variational mode decomposition, VMD)与辅助任务学习的短期负荷预测方法。首先，利用斯皮尔曼等级系数法确定与原始负荷具有强相关性的气象特征。然后，采用变分模态分解算法逐次分离出原始负荷序列中的低频趋势和高频波动。接着，将其与相关气象结合作为辅助任务训练数据输入CNN-BiGRU混合预测模型，并通过共享特征及跨任务注意力机制降低负荷强波动性对负荷预测的影响，实现对原始负荷的准确预测。最后，以我国南方某地区近3年内社会负荷数据为例进行仿真验证。结果表明，所提方法有效降低了日高峰时段负荷的强波动性和随机性对预测模型的影响，提升了负荷预测的准确度。 |
| 关键词: 短期负荷预测 变分模态分解 辅助任务学习 卷积神经网络 双向门控循环单元 |
| DOI：10.19783/j.cnki.pspc.240574 |
| 投稿时间：2024-05-10修订日期：2024-08-23 |
| 基金项目:江苏省国际科技合作项目资助(BZ2021012) |
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| Short-term load forecasting method based on VMD and auxiliary task learning |
| ZHANG Heng1,ZHENG Jianyong1,MEI Fei2,XU Ruilin3 |
| (1. School of Electrical Engineering, Southeast University, Nanjing 210096, China; 2. College of Electrical and Power Engineering,
Hohai University, Nanjing 211100, China; 3. Suzhou Research Institute of Southeast University, Suzhou 215123, China) |
| Abstract: |
| The strong volatility and randomness of load during peak hours significantly affect the accuracy of traditional methods in load forecasting. To address the issue, this paper proposes a short-term load forecasting method based on variational mode decomposition (VMD) and auxiliary task learning. First, using the Spearman rank correlation coefficient method, meteorological features that have a strong correlation with the original load are determined. Subsequently, the VMD algorithm is used to successively separate the low-frequency trends and high-frequency fluctuations in the original load sequence. They are then combined with relevant meteorological data to be used as auxiliary task training data input into the CNN-BiGRU hybrid prediction model. By sharing features and employing a cross-task attention mechanism, the impact of strong load fluctuations on load prediction is reduced, ultimately achieving accurate predictions of the original load. Finally, simulation verification is conducted using load data from a specific region in southern China over the past three years. Results demonstrate that the proposed method effectively reduces the influence of strong volatility and randomness of load during peak hours on the forecasting model, thereby enhancing the accuracy of load forecasting. |
| Key words: short-term load forecasting VMD auxiliary task learning CNN BiGRU |
