A Meta-Survey on Intelligent Energy-Efficient Buildings
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Published:2024-07-30
Issue:8
Volume:8
Page:83
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ISSN:2504-2289
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Container-title:Big Data and Cognitive Computing
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language:en
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Short-container-title:BDCC
Author:
Islam Md Babul12ORCID, Guerrieri Antonio1ORCID, Gravina Raffaele2ORCID, Fortino Giancarlo12ORCID
Affiliation:
1. ICAR-CNR, Institute for High Performance Computing and Networking, National Research Council of Italy, Via P. Bucci 8/9C, 87036 Rende, CS, Italy 2. DIMES, University of Calabria, Via P. Bucci, 87036 Rende, CS, Italy
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.
Funder
SoBigData.it European Union National Research Council of Italy Italian Ministry of University and Research
Reference127 articles.
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