Room energy demand and thermal comfort predictions in early stages of design based on the Machine Learning methods

IF 2.1 Q2 CONSTRUCTION & BUILDING TECHNOLOGY
Nima Forouzandeh, Z. Zomorodian, Zohreh Shaghaghian, Mohamad Tahsildoost
{"title":"Room energy demand and thermal comfort predictions in early stages of design based on the Machine Learning methods","authors":"Nima Forouzandeh, Z. Zomorodian, Zohreh Shaghaghian, Mohamad Tahsildoost","doi":"10.1080/17508975.2022.2049190","DOIUrl":null,"url":null,"abstract":"ABSTRACT Recent studies have focused on data-driven methods for building energy efficiency, by using simulated or empirical data, for energy-based design assessment rather than the common physics-based techniques, which are mostly time-consuming. In this paper, the feasibility of using seven different Machine Learning models, including three single models and four ensemble ones, is studied to predict annual energy demand and thermal comfort of the model. For this purpose, 3024 synthetic samples of a single zone model with seven input features are simulated through the EnergyPlus engine for training in addition to 360 unseen samples as testing data for accuracy reporting. Heating and cooling demands, in addition to five annual thermal comfort indices, are calculated for each data point and used as target indices. Results show Extremely Randomized Trees and Random Forest models had the highest R 2 of 0.99 and 0.85 for cooling and heating demands respectively. Also, the R 2 of these models for predicting annual comfort was between 0.71 and 0.95. Results are then used to develop a prediction framework of thermal comfort and energy demand performance in the early stages of building design, where most of the information about building characteristics is not yet known.","PeriodicalId":45828,"journal":{"name":"Intelligent Buildings International","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Intelligent Buildings International","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/17508975.2022.2049190","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 1

Abstract

ABSTRACT Recent studies have focused on data-driven methods for building energy efficiency, by using simulated or empirical data, for energy-based design assessment rather than the common physics-based techniques, which are mostly time-consuming. In this paper, the feasibility of using seven different Machine Learning models, including three single models and four ensemble ones, is studied to predict annual energy demand and thermal comfort of the model. For this purpose, 3024 synthetic samples of a single zone model with seven input features are simulated through the EnergyPlus engine for training in addition to 360 unseen samples as testing data for accuracy reporting. Heating and cooling demands, in addition to five annual thermal comfort indices, are calculated for each data point and used as target indices. Results show Extremely Randomized Trees and Random Forest models had the highest R 2 of 0.99 and 0.85 for cooling and heating demands respectively. Also, the R 2 of these models for predicting annual comfort was between 0.71 and 0.95. Results are then used to develop a prediction framework of thermal comfort and energy demand performance in the early stages of building design, where most of the information about building characteristics is not yet known.
基于机器学习方法的设计早期房间能量需求和热舒适性预测
最近的研究主要集中在数据驱动的建筑节能方法上,通过使用模拟或经验数据来进行基于能源的设计评估,而不是常见的基于物理的技术,这通常是耗时的。本文研究了使用7种不同的机器学习模型(包括3种单一模型和4种集成模型)预测模型年能源需求和热舒适的可行性。为此,通过EnergyPlus引擎模拟具有7个输入特征的单个区域模型的3024个合成样本进行训练,另外360个未见样本作为准确性报告的测试数据。采暖和制冷需求,以及五个年度热舒适指数,计算每个数据点,并作为目标指标。结果表明,极端随机树模型和随机森林模型的制冷和制热需求的r2值最高,分别为0.99和0.85。这些模型预测年舒适度的r2在0.71 ~ 0.95之间。然后,研究结果用于开发建筑设计早期阶段的热舒适和能源需求性能预测框架,在这个阶段,大多数关于建筑特征的信息还不为人所知。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Intelligent Buildings International
Intelligent Buildings International CONSTRUCTION & BUILDING TECHNOLOGY-
CiteScore
4.60
自引率
4.30%
发文量
8
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信