基于MVMD-SSA-LSTM的建筑短期供暖负荷预测
DOI:
https://doi.org/10.18686/cncest356关键词:
建筑供暖负荷预测；多变量变分模态分解；长短期记忆神经网络；麻雀搜索算法摘要
建筑短期供暖负荷预测是能源系统后续控制的关键步骤，直接影响系统的能耗。然而，由于供暖负荷及其影响因素构成波动的时间序列数据，数据中的噪声干扰显著限制了预测的准确性和稳定性。为了解决这一问题，本文提出了一种新的MVMD-SSA-LSTM模型用于建筑供暖负荷预测，该模型结合了多变量变分模态分解（Multivariate Variational Mode Decomposition, MVMD）、麻雀搜索算法（Sparrow Search Algorithm, SSA）和长短期记忆（Long Short-Term Memory, LSTM）神经网络。首先，对建筑供暖负荷的影响因素进行相关性分析，以确定关键因素。随后，使用MVMD将多维数据集分解为多个模态。然后对这些分解的模态进行相关性分析，提取补充特征，并将其与原始数据结合形成新的数据集，从而减少特征冗余。最后，使用LSTM神经网络作为核心预测模型，并通过SSA算法优化三个关键参数：最大训练迭代次数、隐藏单元数量和初始学习率。将每个供暖负荷模态的预测输出汇总，得到最终预测结果。结果表明，MVMD-SSA-LSTM模型有效缓解了供暖负荷序列预测中的不确定性，克服了噪声干扰，并表现出优于其他常用模型的性能，具有显著更高的准确性和稳定性。
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