Volume 3,Issue 2
考虑可再生能源绿证交易的LSTNet 虚拟电厂负荷预测
【目的】旨在解决可再生能源绿证交易背景下虚拟电厂中长期负荷预测的精度问题，针对数据缺失、算法运行时间长等挑战，重点探究绿证交易对负荷变化的影响机理，并优化预测模型以提升精准度。【方法】首先通过皮尔逊相关性分析验证负荷与非同步发电瞬时渗透率（SNSP）的中等相关性（相关系数0.410），证明绿证交易对负荷存在影响；利用密度聚类算法（DBSCAN）提取负荷季节性特征（分为春夏/ 秋冬两类），缩减训练数据规模。在此基础上，提出DBSCAN- LSTNet 混合预测模型：采用一维CNN 提取短期时间序列特征，结合GRU 和Skip-GRU 捕获长周期依赖关系，并通过自回归模块（AR）解决非线性特征导致的尺度敏感性问题。以SMAPE 为评价指标，使用北爱尔兰2018 年–2020 年负荷数据进行训练和验算，并引入SNSP 表征绿证交易强度。【结果】实验表明：（1）考虑绿证交易因素（SNSP）的DBSCAN-LSTNet 模型误差降至2.56%（未考虑时为6.03%），显著优于传统的LSTM（3.91%）和SVM（23.45%）；（2）绿证因素可使预测误差平均降低4%；（3）DBSCAN 有效缩减数据规模，模型训练效率提升，且对离群点具有鲁棒性；（4）LSTNet 融合线性和非线性预测，比单一LSTM 具有更高精度与鲁棒性。【结论】虚拟电厂负荷预测需纳入绿证交易等市场因素。所提DBSCAN- LSTNet 模型通过特征降维和混合神经网络结构，实现了高精度中长期负荷预测（SMAPE≤2.56%），为电力市场决策提供可靠依据。
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