Smart City Gnosys
Smart city article details
| Title | A Meta-Survey On Intelligent Energy-Efficient Buildings |
|---|---|
| ID_Doc | 2539 |
| Authors | Islam M.B.; Guerrieri A.; Gravina R.; Fortino G. |
| Year | 2024 |
| Published | Big Data and Cognitive Computing, 8, 8 |
| DOI | http://dx.doi.org/10.3390/bdcc8080083 |
| Abstract | The rise of the Internet of Things (IoT) has enabled the development of smart cities, intelligent buildings, and advanced industrial ecosystems. When the IoT is matched with machine learning (ML), the advantages of the resulting enhanced environments can span, for example, from energy optimization to security improvement and comfort enhancement. Together, IoT and ML technologies are widely used in smart buildings, in particular, to reduce energy consumption and create Intelligent Energy-Efficient Buildings (IEEBs). In IEEBs, ML models are typically used to analyze and predict various factors such as temperature, humidity, light, occupancy, and human behavior with the aim of optimizing building systems. In the literature, many review papers have been presented so far in the field of IEEBs. Such papers mostly focus on specific subfields of ML or on a limited number of papers. This paper presents a systematic meta-survey, i.e., a review of review articles, that compares the state of the art in the field of IEEBs using the Prisma approach. In more detail, our meta-survey aims to give a broader view, with respect to the already published surveys, of the state-of-the-art in the IEEB field, investigating the use of supervised, unsupervised, semi-supervised, and self-supervised models in a variety of IEEB-based scenarios. Moreover, our paper aims to compare the already published surveys by answering five important research questions about IEEB definitions, architectures, methods/models used, datasets and real implementations utilized, and main challenges/research directions defined. This meta-survey provides insights that are useful both for newcomers to the field and for researchers who want to learn more about the methodologies and technologies used for IEEBs’ design and implementation. © 2024 by the authors. |
| Author Keywords | deep learning; energy efficiency; intelligent energy-efficient building; Internet of Things (IoT); machine learning; meta-survey; reinforcement learning; smart building |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
5161  | 0.911 | Masroor M.; Rezazadeh J.; Ayoade J.; Aliehyaei M. | A Survey Of Intelligent Building Automation With Machine Learning And Iot | Advances in Building Energy Research, 17, 3 (2023) |
| 32375 | 0.907 | Nikpour M.; Yousefi P.B.; Jafarzadeh H.; Danesh K.; Shomali R.; Asadi S.; Lonbar A.G.; Ahmadi M. | Intelligent Energy Management With Iot Framework In Smart Cities Using Intelligent Analysis: An Application Of Machine Learning Methods For Complex Networks And Systems | Journal of Network and Computer Applications, 235 (2025) |
| 32444 | 0.904 | Nijim M.; Kanumuri V.; Albetaineh H.; Goyal A. | Intelligent Monitoring And Management Of Smart Buildings Using Machine Learning: Optimizing User Behavior And Energy Efficiency | Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023 (2023) |
| 24850 | 0.9 | Shahrabani M.M.N.; Apanaviciene R. | Evaluation Of Smart Building Integration Into A Smart City By Applying Machine Learning Techniques | Buildings, 15, 12 (2025) |
| 8877 | 0.89 | Hemlata; Rai M. | An Optimized Demand For Cost And Environment Benefits Towards Smart Residentials Using Iot And Machine Learning | Sustainable Smart Homes and Buildings with Internet of Things (2024) |
| 45664 | 0.889 | Chen B.; Ye X.; Dai F. | Research On Sustainable Building Development In The Context Of Smart Cities: Based On Citespace, Vosviewer, And Bibliometrix | Buildings, 15, 11 (2025) |
| 51061 | 0.883 | Arabasy M.; Hussein M.F.; Abu Osba R.; Al Dweik S. | Smart Housing: Integrating Machine Learning In Sustainable Urban Planning, Interior Design, And Development | Asian Journal of Civil Engineering, 26, 1 (2025) |
| 6495 | 0.881 | Fan C.; Yan D.; Xiao F.; Li A.; An J.; Kang X. | Advanced Data Analytics For Enhancing Building Performances: From Data-Driven To Big Data-Driven Approaches | Building Simulation, 14, 1 (2021) |
| 49215 | 0.88 | Swathika O.V.G.; Karthikeyan K.; Padmanaban S. | Smart Buildings Digitalization: Iot And Energy Efficient Smart Buildings Architecture And Applications | Smart Buildings Digitalization: IoT and Energy Efficient Smart Buildings Architecture and Applications (2022) |
| 5629 | 0.88 | Gonzalez-Vidal A.; Mendoza-Bernal J.; Niu S.; Skarmeta A.F.; Song H. | A Transfer Learning Framework For Predictive Energy-Related Scenarios In Smart Buildings | IEEE Transactions on Industry Applications, 59, 1 (2023) |