| 宫飞翔,陈宋宋,罗鑫宇,李 彬.考虑多模态分解的融合模型空调负荷可调潜力分析[J].电力需求侧管理,2025,27(2):48-54 |
| 考虑多模态分解的融合模型空调负荷可调潜力分析 |
| Air conditioning load adjustable potential analysis of fusion model considering multimodal decomposition |
| 投稿时间：2024-11-09 修订日期：2025-01-11 |
| DOI：10. 3969 / j. issn. 1009-1831. 2025. 02. 008 |
| 中文关键词: 柔性负荷 模态分解 鲸鱼算法 卷积神经网络 双向长短期记忆网络 |
| 英文关键词: flexible load modal decomposition whale algorithm CNN BiLSTM |
| 基金项目:国家电网有限公司科技项目（5108-202218280A-2-389-XG） |
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| 摘要点击次数: 48 |
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| 中文摘要: |
| 柔性负荷资源因其响应迅速和调节灵活的特点,能够在确保用户舒适度不受显著影响的前提下,迅速对电网调度作出反应。空调负荷作为柔性负荷的核心部分,可以通过科学的调控策略来削减高峰电力需求,进而缓解电力供应压力。鉴于空调负荷数据具有非线性和特征模糊等特性,提出了一种结合模态分解与神经网络的空调负荷预测模型。首先采用皮尔逊相关系数来构建相似的周负荷序列。随后运用自适应噪声完备集合经验模态分解与变分模态分解（variational mode decomposition,VMD）技术对负荷进行分解。在VMD环节,将原始时间序列信号输入VMD层,通过VMD算法将其分解为多个本征模函数（intomultiple eigenmode functions,IMFs）。将这些IMFs分别输入卷积神经网络,通过卷积、激活和池化等操作提取其局部特征。之后这些特征向量被送入双向长短期记忆网络（BiLSTM）,利用其双向传播能力来捕捉序列中的长期依赖性。并使用改进的鲸鱼算法优化超参数,最终在输出预测负荷序列的基础上,进一步探讨了负荷的调节潜力。实验结果显示,本方法不仅预测速度快、精度高,而且能更清晰地揭示负荷的调节潜力。 |
| 英文摘要: |
| Flexible load resources can respond to power grid dispatching quickly without significant impact on user comfort because of their rapid response and flexible regulation. As the core part of flexible load, air conditioning load can reduce the peak power demand through scientific control strategy, and then relieve the pressure of power supply. In view of the nonlinear and fuzzy characteristics of air conditioning load data, a model of air conditioning load prediction based on modal decomposition and neural network is proposed. First,Pearson correlation coefficients are used to construct similar weekly load sequences. Then the load is decomposed by adaptive noise complete set empirical mode decomposition and variational mode decomposition（VMD）. In the VMD section, the original time series signal is input into the VMD layer and decomposed into multiple eigenmode functions（IMFs）by the VMD algorithm. These IMFs are input into convolutional neural network respectively, and their local features are extracted by convolutional, activation and pooling operations. These feature vectors are then fed into a bidirectional long short-term memory network, which uses its bidirectional propagation capability to capture long-term dependencies in the sequence. The improved whale algorithm is used to optimize the hyperparameters, and the regulation potential of the load is further discussed on the basis of the output forecast load sequence. The experimental results show that this method not only has high forecasting speed and accuracy, but also can reveal the adjustment potential of load more clearly. |
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