The current ultra-short-term cooling load forecasting models have not given due attention to the data pre-processing stage. In this paper, multivariate signal decomposition methods MEMD and MvFIF are used in the preprocessing phase to replace the complex signal with simpler subcomponents. The resulting increase in the number of features is tackled through a dimensionality reduction technique, PCA. Finally, prediction is done using two rigorous machine learning algorithms – LSTM and XGBoost. By combining these algorithms at different stages, four hybrid algorithms are formed - MEMD-PCA-LSTM, MEMD-PCA-XGBoost and MvFIF-PCA-LSTM, and MvFIF-PCA-XGBoost. Following a thorough performance comparison, this paper proposes MvFIF-PCA-LSTM for the prediction of ultra-short-term cooling loads. Additionally, experiments are performed to compare the running time of the proposed model, to endorse the importance of using PCA in the proposed model, and to evaluate the choice of parameters that undergo feature reduction. Compared to the base LSTM model assayed on the same datasets, the proposed model offered an improvement of 24.94%, 33.65%, and 23.82% in R2 values for SIT@Dover, SIT@NYP, and simulated datasets, respectively. MAPE achieved by the proposed model is exceptionally low, measuring at 1.13% for the SIT@Dover dataset, 1.42% for the SIT@NYP dataset, and a mere 0.36% for the simulated dataset. The best values of performance metrics computed for the proposed model demonstrate its accuracy in ultra-short-term cooling load prediction.

中文翻译：

---

基于多元快速迭代滤波和长短期记忆的超短期冷负荷预测混合模型

目前的超短期冷负荷预测模型没有对数据预处理阶段给予应有的重视。本文在预处理阶段使用多元信号分解方法MEMD和MvFIF，用更简单的子分量代替复杂信号。由此产生的特征数量增加可以通过降维技术 PCA 来解决。最后，使用两种严格的机器学习算法（LSTM 和 XGBoost）完成预测。通过在不同阶段组合这些算法，形成了四种混合算法——MEMD-PCA-LSTM、MEMD-PCA-XGBoost和MvFIF-PCA-LSTM以及MvFIF-PCA-XGBoost。经过彻底的性能比较，本文提出了 MvFIF-PCA-LSTM 来预测超短期冷负荷。此外，还进行了实验来比较所提出模型的运行时间，以认可在所提出模型中使用 PCA 的重要性，并评估进行特征缩减的参数选择。与在相同数据集上测试的基本 LSTM 模型相比，所提出的模型在 SIT@Dover、SIT@NYP 和模拟数据集的 R2 值上分别提高了 24.94%、33.65% 和 23.82%。所提出的模型实现的 MAPE 非常低，SIT@Dover 数据集为 1.13%，SIT@NYP 数据集为 1.42%，模拟数据集仅为 0.36%。为所提出的模型计算的性能指标的最佳值证明了其在超短期冷负荷预测方面的准确性。