基于“分解 - 预测 - 聚合”及 GA 优化的碳价格预测方法研究

李 晶磊(内蒙古工业大学,中国)

DOI: http://dx.doi.org/10.12349/tie.v2i7.8098

Article ID: 8098

摘要


“碳中和”背景下,碳价格精准预测对数据中心至关重要。碳价序列非线性非平稳,单一模型精度受限。现有“分解-聚合”范式中变分模态分解技术(VMD)参数()设定主观,影响精度。为此,本文提出VMD-GA-BiLSTM模型。核心创新在于:引入遗传算法,以最小化本征模态分量的平均近似熵为适应度函数,自适应寻优VMD最佳参数,以提升序列可预测性。随后利用双向长短期记忆网络进行独立预测各分量并聚合。选取欧盟碳市场数据实证表明,所提模型在均方根误差、平均绝对误差上均优于自回归积分滑动平均模型、双向长短期记忆网络(BiLSTM)及未优化VMD-BiLSTM等基准。本研究为高波动序列预测提供了严谨范式,为数据中心低碳调度提供了可靠的价格信号。

关键词


碳价格预测;数据中心;分解-预测-聚合;变分模态分解(VMD);遗传算法(GA);近似熵(ApEn);双向长短期记忆网络(BiLSTM)

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