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A large dataset is collected and pre-processed, multiple models are trained and validated via cross-validation, and neural networks show strong predictive accuracy. Model interpretability and future directions, including real-time monitoring data, are also discussed, enabling more intelligent design and operation.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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problem does this paper address in the building industry?","Question",{"text":63,"@type":64},"It targets the need to optimize building energy efficiency for sustainable development, where traditional empirical methods often cannot predict energy consumption patterns in complex environments.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How does the proposed approach use machine learning?",{"text":68,"@type":64},"It analyzes energy consumption, environmental parameters, building characteristics, and user behavior to predict and improve building energy efficiency through trained machine learning models, including neural networks.",{"name":70,"@type":61,"acceptedAnswer":71},"What do the results indicate about model performance and future work?",{"text":72,"@type":64},"The neural network demonstrates clear advantages in energy-efficiency prediction, and the paper discusses practical interpretability as well as future research directions such as adding real-time monitoring 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AND WATER ENGINEERING  \nDOI: 10.24425/ace.2025.153353  \nARCHIVES OF CIVIL ENGINEERING  \nPOLISH ACADEMY OF SCIENCES ISSN 1230-2945 Vol. LXXI ISSUE 1 2025  \n© 2025. Chun Gu. pp. 615–629  \nThis is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (CC BY-NC-ND 4.0, [https://creativecommons.org/licenses/by-nc-nd/4.0/](https://creativecommons.org/licenses/by-nc-nd/4.0/)), which permits use, distribution, and reproduction in any medium, provided that the Article is properly cited, the use is non-commercial, and no modiﬁcations or adaptations are made.  \nResearch paper  \nOptimize building energy eﬃciency design and evaluation  \nwith machine learning  \nChun Gu1  \nAbstract: With the increasing demand for energy eﬃciency optimization in the building industry, this study explores the application of machine learning technology in building energy eﬃciency design and evaluation. By comprehensively analyzing energy consumption data, environmental factors, building characteristics, and user behavior patterns, this paper proposes a machine learning-based approach aimed at accurately predicting and improving the energy eﬃciency of buildings. The study collected and pre-processed a large amount of data, built and trained multiple models, including neural networks, which showed a high degree of predictive accuracy in cross-validation. The results show that the neural network has obvious advantagesin the task of building energy eﬃciency prediction. In addition, the interpretability of the model in practical applications and future research directions, such as the introduction of real-time monitoring data and in-depth study of the interpretability of the model, are also discussed. This study not only provides a new perspective for building energy eﬃciency optimization, but also provides a practical tool for intelligent building design and operation.  \nKeywords: building energy eﬃciency, energy consumption forecast, energy eﬃciency design and evaluation, machine learning  \n1Mas., Architectural Engineering Institute, Zhoukou Vocational and Technical College, Zhoukou 466000, China, e-mail: [guchun188@outlook.com](guchun188@outlook.com), ORCID: 0009-0004-8795-4794  \n616 C. GU   \n1. Introduction  \nIn the current construction industry, building energy eﬃciency has become an increasingly important issue. With the increasing global concern for sustainable development and environmental protection, the optimization of building energy consumption has become particularly critical. As one of the major sectors of global energy consumption, the construction industry has great potential for energy eﬃciency improvement, but also faces challenges. Traditional energy eﬃciency design methods rely on empirical rules and simpliﬁed models, which often fail to accurately predict energy consumption patterns in complex built environments. In addition, in recent years, machine learning technology has shown signiﬁcant potential in several areas, especially in processing large amounts of data and solving complex pattern recognition problems. Its capabilities also show great application prospects in building energy eﬃciency optimization. Machine learning can process complex data from a building, such as environmental parameters, usage patterns, material properties, etc., to predict and optimize a building’s energy consumption.  \nThere has been a lot of research exploring the application of machine learning in the ﬁeld of energy eﬃcient building design and evaluation. Liang et al. emphasized the importance of energy eﬃciency models in the carbon neutral design of buildings, indicating that considering energy eﬃciency at the building design stage can signiﬁcantly improve the sustainability of buildings [1] . Similarly, Buzatu et al. showed through experimental evaluation that an integrated design approach is cruc","cbCaie835jeBTcPd","https://ap.wps.com/l/cbCaie835jeBTcPd","pdf",960050,15,"English","# Abstract\n## 1. Introduction\n## Related Work\n## Research Objective and Methodology","[{\"question\":\"What problem does this paper address in the building industry?\",\"answer\":\"It targets the need to optimize building energy efficiency for sustainable development, where traditional empirical methods often cannot predict energy consumption patterns in complex environments.\"},{\"question\":\"How does the proposed approach use machine learning?\",\"answer\":\"It analyzes energy consumption, environmental parameters, building characteristics, and user behavior to predict and improve building energy efficiency through trained machine learning models, including neural networks.\"},{\"question\":\"What do the results indicate about model performance and future work?\",\"answer\":\"The neural network demonstrates clear advantages in energy-efficiency prediction, and the paper discusses practical interpretability as well as future research directions such as adding real-time monitoring data.\"}]","Optimize building energy efficiency design and evaluation with machine learning | PDF",38]