Efficiency in building energy use: Pattern discovery and crisis identification in hot-water consumption data

IF 6.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Energy and Buildings Pub Date : 2025-03-10 DOI:10.1016/j.enbuild.2025.115579
Lina Morkunaite , Darius Pupeikis , Nikolaos Tsalikidis , Marius Ivaskevicius , Fallon Clare Manhanga , Jurgita Cerneckiene , Paulius Spudys , Paraskevas Koukaras , Dimosthenis Ioannidis , Agis Papadopoulos , Paris Fokaides
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Abstract

As global challenges such as climate change and pandemics increasingly disrupt urban systems, the need for efficient and resilient management of energy resources has become critical. The energy used to prepare domestic hot water (DHW) takes a large proportion of residential buildings’ total thermal energy demand. However, it is often overlooked in research due to its stochastic nature and high dependence on user behaviour. This study explores the identification of the crisis and its severity level in the DHW consumption data and the corresponding control actions necessary to mitigate its impact. To identify crisis severity, we utilised the mobility data of retail/recreation activities and transit stations, making the results generalisable for any crisis. In addition, we used power consumption for DHW preparation data from 10 residential apartment buildings located in Kaunas city to develop a machine learning-based hybrid ensembling stacking classifier (ESC) capable of predicting the crisis and its severity level. Finally, we applied principal component analysis (PCA) and k-means clustering to categorise DHW consumption hours throughout the day for each severity level. The results showed that the developed ESC classifier significantly outperforms (R2=0.99) the baseline LGBMC classifier (R2=0.92). Combining the classifier with extracted daily consumption patterns and clusters allows the optimisation of control actions on the supply, distribution, and demand side of the DHW system.
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随着气候变化和大流行病等全球性挑战对城市系统的破坏日益严重,对能源资源进行高效和弹性管理已变得至关重要。在住宅建筑的总热能需求中,用于制备生活热水(DHW)的能源占很大比例。然而,由于其随机性和对用户行为的高度依赖性,在研究中经常被忽视。本研究探讨了如何识别 DHW 消耗数据中的危机及其严重程度,以及为减轻其影响所需的相应控制措施。为了识别危机的严重程度,我们利用了零售/娱乐活动和中转站的流动性数据,从而使研究结果适用于任何危机。此外,我们还使用了考纳斯市 10 栋住宅公寓楼的热水供应耗电量数据,开发出一种基于机器学习的混合集合堆叠分类器(ESC),能够预测危机及其严重程度。最后,我们应用主成分分析 (PCA) 和 k-means 聚类对每个严重程度的全天 DHW 消耗时间进行了分类。结果显示,所开发的 ESC 分类器明显优于(R2=0.99)基线 LGBMC 分类器(R2=0.92)。将分类器与提取的每日消耗模式和集群相结合,可以优化 DHW 系统供配电和需求方的控制措施。
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来源期刊
Energy and Buildings
Energy and Buildings 工程技术-工程:土木
CiteScore
12.70
自引率
11.90%
发文量
863
审稿时长
38 days
期刊介绍: An international journal devoted to investigations of energy use and efficiency in buildings Energy and Buildings is an international journal publishing articles with explicit links to energy use in buildings. The aim is to present new research results, and new proven practice aimed at reducing the energy needs of a building and improving indoor environment quality.
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