Comparative review of hybrid forecasting models for short-term prediction of building thermal load

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Computer Science > Machine Learning arXiv:2610.06881 (cs) [Submitted on 19 Sep 2026 (v1), last revised 7 Oct 2026 (this version, v2)] Title: Comparative review of hybrid forecasting models for short-term prediction of building thermal load Authors: Nikolaos A. Efkarpidis, Despoina Kothona, Georgios C. Christoforidis.

计算机科学 > 机器学习 arXiv:2610.06881 (cs) [提交于 2026 年 9 月 19 日 (v1)，最后修订于 2026 年 10 月 7 日 (本版本，v2)] 标题：建筑热负荷短期预测混合预测模型的比较综述作者：Nikolaos A. Efkarpidis, Despoina Kothona, Georgios C. Christoforidis。

Abstract: In this paper, a comparative review of different hybrid models for short-term forecasting of building thermal demand is carried out. Particularly, the assessment tackles the comparison of data-driven models enhanced with other state-of-the-art techniques. At the first step, the existing techniques reported in the literature are analysed. It is concluded that Metaheuristics or a data-driven model are used to identify the parameters of the basic model. The qualitative evaluation includes for each method the input and output features, main advantages and drawbacks.

摘要：本文对用于建筑热需求短期预测的不同混合模型进行了比较综述。评估工作特别针对了通过其他前沿技术增强的数据驱动模型进行了比较。第一步，对文献中报道的现有技术进行了分析。结论是，元启发式算法或数据驱动模型被用于识别基础模型的参数。定性评估包括每种方法的输入和输出特征、主要优点及缺点。

At the second step, an existing dataset of historical thermal demand from Scottish households, as well as historical weather forecasts are utilized to assess additionally the performance of existing hybrid methods. From the assessment of 13 hybrid methods, the Empirical Modal Decomposition - long short-term memory - Markov (EMD-LSTM-Markov) model can predict with the highest accuracy the day-ahead power pattern of heating and domestic hot water (DHW) demands. Though local power peaks are also accurately predicted, high power swells and spikes are underestimated.

第二步，利用苏格兰家庭历史热需求的现有数据集以及历史天气预报，对现有混合方法的性能进行了额外评估。在对 13 种混合方法的评估中，经验模态分解-长短期记忆-马尔可夫（EMD-LSTM-Markov）模型能够以最高精度预测供暖和生活热水（DHW）需求的日前功率模式。尽管局部功率峰值也能被准确预测，但高功率浪涌和尖峰被低估了。

Other methods, such as Support Vector Machine - Simulated Annealing (SVM-SA) and Random Forest - Improved Sparrow Search Algorithm - LSTM (RF-ISSA-LSTM) predict a smooth pattern of heating and DHW demand profiles with rapid changes underestimating most power peaks.

其他方法，如支持向量机-模拟退火（SVM-SA）和随机森林-改进麻雀搜索算法-LSTM（RF-ISSA-LSTM），预测出的供暖和生活热水需求曲线较为平滑，但在快速变化的情况下低估了大多数功率峰值。