使用预测分析的建筑能源建模和管理新模型:分区分层多任务回归(PHMR)

IF 4.3 2区 环境科学与生态学 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Indoor air Pub Date : 2024-03-11 DOI:10.1155/2024/5595459
Shuluo Ning, Hyunsoo Yoon
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引用次数: 0

摘要

建筑是能源消耗大户,在全球能源使用总量中占有相当大的比例。大量的能源消耗不仅导致运营成本增加,还引发了温室气体排放等环境问题。在美国,建筑能耗约占总能耗的 40%,这凸显了高效能源管理的重要性。因此,准确预测建筑能耗至关重要。然而,由于室内外变量之间错综复杂的相互作用,准确预测建筑能耗仍然是一项挑战。本研究介绍了分区分层多任务回归(PHMR),这是一种将递归分区回归(RPR)与多任务学习(hierML)相结合的创新模型。PHMR 综合了室内因素(如建筑设计和运行变量)和室外环境影响因素,能很好地预测建筑能耗。严格的模拟研究证明了 PHMR 的功效。它优于传统的单一预测回归模型,预测准确率提高了 32.88% 至 41.80%,尤其是在训练数据有限的情况下。这凸显了 PHMR 的鲁棒性和适应性。在西班牙,PHMR 在管理模块化房屋的供暖、通风和空调(HVAC)系统中的实际应用使预测准确率提高了 37%。PHMR 预测值与实际能耗之间的皮尔逊相关系数高达 0.8,证明了效率的大幅提升。PHMR 不仅能精确预测能源消耗,还有助于降低运营成本,从而提高建筑能源管理的可持续性。该模型在实际环境中的应用表明,它有潜力成为建筑师、工程师和设施管理人员设计和维护节能建筑的重要工具。该模型将全面的数据分析与特定领域的知识相结合,使其成为推动建筑领域可持续能源实践的重要资产。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A New Model for Building Energy Modeling and Management Using Predictive Analytics: Partitioned Hierarchical Multitask Regression (PHMR)

Buildings are major consumers of energy, accounting for a significant proportion of total energy use worldwide. This substantial energy consumption not only leads to increased operational costs but also contributes to environmental concerns such as greenhouse gas emissions. In the United States, building energy consumption accounts for about 40% of total energy use, highlighting the importance of efficient energy management. Therefore, accurate prediction of energy usage in buildings is crucial. However, accurate prediction of building energy consumption remains a challenge due to the intricate interaction of indoor and outdoor variables. This study introduces the Partitioned Hierarchical Multitask Regression (PHMR), an innovative model integrating recursive partition regression (RPR) with multitask learning (hierML). PHMR adeptly predicts building energy consumption by integrating both indoor factors, such as building design and operational variables, and outdoor environmental influences. Rigorous simulation studies illustrate PHMR’s efficacy. It outperforms traditional single-predictor regression models, achieving a 32.88% to 41.80% higher prediction accuracy, especially in scenarios with limited training data. This highlights PHMR’s robustness and adaptability. The practical application of PHMR in managing a modular house’s Heating, Ventilation, and Air Conditioning (HVAC) system in Spain resulted in a 37% improvement in prediction accuracy. This significant efficiency gain is evidenced by a high Pearson correlation coefficient (0.8) between PHMR’s predictions and actual energy consumption. PHMR not only offers precise predictions for energy consumption but also facilitates operational cost reductions, thereby enhancing sustainability in building energy management. Its application in a real-world setting demonstrates the model’s potential as a valuable tool for architects, engineers, and facility managers in designing and maintaining energy-efficient buildings. The model’s integration of comprehensive data analysis with domain-specific knowledge positions it as a crucial asset in advancing sustainable energy practices in the building sector.

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来源期刊
Indoor air
Indoor air 环境科学-工程:环境
CiteScore
10.80
自引率
10.30%
发文量
175
审稿时长
3 months
期刊介绍: The quality of the environment within buildings is a topic of major importance for public health. Indoor Air provides a location for reporting original research results in the broad area defined by the indoor environment of non-industrial buildings. An international journal with multidisciplinary content, Indoor Air publishes papers reflecting the broad categories of interest in this field: health effects; thermal comfort; monitoring and modelling; source characterization; ventilation and other environmental control techniques. The research results present the basic information to allow designers, building owners, and operators to provide a healthy and comfortable environment for building occupants, as well as giving medical practitioners information on how to deal with illnesses related to the indoor environment.
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