基于机器学习的智能建筑能源管理系统能耗预测

IF 3.6 Q1 ENGINEERING, CIVIL Journal of Information Technology in Construction Pub Date : 2023-09-22 DOI:10.36680/j.itcon.2023.033
Mustika Sari, Mohammed Ali Berawi, Teuku Yuri Zagloel, Nunik Madyaningarum, Perdana Miraj, Ardiansyah Ramadhan Pranoto, Bambang Susantono, Roy Woodhead
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引用次数: 1

摘要

智能楼宇是一种利用数码及通讯技术改善楼宇内住户舒适度及提高楼宇运作能源使用效率的楼宇发展方法。尽管有诸多好处,但智能建筑的概念仍被缓慢采用,尤其是在发展中国家。机器学习(ML)等计算技术的进步帮助业主在建筑设计过程中更准确地模拟和优化各种建筑性能。因此,本研究旨在通过识别智能建筑特征的特征来帮助建筑节能设计策略,这些特征可能会促进建筑节能。此外,基于所识别的特征,然后开发一个ML模型来预测能源使用水平。采用k-最近邻(k-NN)算法,以朱拉隆功大学建筑能源管理系统(CU-BEMS)中可公开获取的智能建筑能源使用数据集作为训练和测试数据集,开发模型。验证结果表明,该预测模型的平均相对误差值为17.76%。根据所审查的地板,通过应用已确定的特征获得的能源效率水平从34.5%到45.3%不等。本文还提出了基于机器学习的智能建筑能源管理的仪表板界面设计。
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Machine learning-based energy use prediction for the smart building energy management system
Smart building is a building development approach utilizing digital and communication technology to improve occupants' comfort inside the building and help increase energy usage efficiency in building operations. Despite its benefits, the smart building concept is still slowly adopted, particularly in developing countries. The advancement of computational techniques such as machine learning (ML) has helped building owners simulate and optimize various building performances in the building design process more accurately. Therefore, this study aims to assist energy efficiency design strategies in a building by identifying the features of the smart building characteristics that can potentially foster building energy efficiency. Furthermore, an ML model based on the features identified is then developed to predict the level of energy use. K-Nearest Neighbor (k-NN) algorithm is employed to develop the model with the openly accessible smart building energy usage datasets from Chulalongkorn University Building Energy Management System (CU-BEMS) as the training and testing datasets. The validation result shows that the predictive model has an average relative error value of 17.76%. The energy efficiency levels obtained from applying identified features range from 34.5% to 45.3%, depending on the reviewed floor. This paper also proposed the dashboard interface design for ML-based smart building energy management.
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来源期刊
CiteScore
6.90
自引率
8.60%
发文量
44
审稿时长
26 weeks
期刊最新文献
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