混合建模及其在区域供热系统建筑负荷预测中的可移植性研究

IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Engineering Applications of Artificial Intelligence Pub Date : 2024-11-04 DOI:10.1016/j.engappai.2024.109544
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引用次数: 0

摘要

在区域供热系统中,这些区域内建筑物的历史运行数据可能部分或全部缺失。传统的数据驱动模型很难预测真实结果,因为没有历史数据用于模型训练。然而,利用基于物理的方法进行负荷计算需要较长的处理时间,并且会遇到精度较低的问题。本文研究了几种将数据驱动模型和基于物理的模型与不同的融合方法相结合的混合模型。基于物理的模型分别根据傅立叶定律和大规范集合理论计算围护荷载和渗透荷载。经过负荷处理、特征融合和残差连接后,最佳的高级混合模型与数据驱动模型相比,预测结果分别提高了 21.35%、16.35% 和 12.73%。此外,高级混合模型在所有数据量组之间也具有很强的可移植性。在实际应用方面,先进的混合模型可以在有限的数据场景中有效泛化,并具有强大的转移能力。通过混合建模构建的最佳模型性能最高,并能节省总的训练成本,具有很强的可移植性。
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Investigation of hybrid modeling and its transferability in building load prediction used for district heating systems
In the district heating systems, the historical operation data of the buildings in those areas would be partially or entirely missing. The traditional data-driven model is hard to predict the ground truth results because the historical data is not available for model training. However, utilizing the physics-based methods for load calculation takes a long time to process and encounters low accuracy issues. This paper investigates several hybrid models that integrate the data-driven model and the physics-based models with different fusion methods. The physics-based models calculate envelope load and infiltration load, based on Fourier's law and the grand canonical ensemble theory, respectively. After undergoing load processing, features fusion, and residual connection, the best advanced hybrid models generate 21.35%, 16.35%, and 12.73% better prediction results compared with the data-driven model. Moreover, the advanced hybride models also perform strong transferability across all the data quantity groups. In terms of practical application, the advanced hybrid models could be deployed with effective generalization in limited data scenarios and robust transfer capabilities. The selected best model constructed by hybrid modeling displays the highest performance and saves the total training costs with strong transferability.
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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