建筑热负荷短期预测混合模型比较综述
Comparative review of hybrid forecasting models for short-term prediction of building thermal load
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- VIVAVIS Schweiz AG（VIVAVIS瑞士公司）
- University of Western Macedonia（西马其顿大学）
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中文总结 AI 辅助
本文比较了13种混合预测模型，发现EMD-LSTM-Markov模型在建筑热负荷短期预测中精度最高，但高功率尖峰仍被低估。
中文摘要 AI 辅助
本文对不同混合模型在建筑热需求短期预测中的应用进行了比较综述。特别地，评估着重于将数据驱动模型与其他先进技术相结合的模型比较。第一步，分析了文献中已有的技术。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、主要优点和缺点。第二步，利用苏格兰家庭历史热需求数据集以及历史天气预报数据，进一步评估现有混合方法的性能。通过对13种混合方法的评估，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日前供暖和生活热水（DHW）需求的功率模式。尽管局部功率峰值也能被准确预测，但高功率涌浪和尖峰被低估。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-长短期记忆（RF-ISSA-LSTM），预测的供暖和DHW需求曲线平滑，但快速变化时多数功率峰值被低估。
英文摘要
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. 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. 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.
