Machine Learning Techniques in the Energy Consumption of Buildings: A Systematic Literature Review Using Text Mining and Bibliometric Analysis
The high level of energy consumption of buildings is significantly influencing occupant behavior changes towards improved energy efficiency. This paper introduces a systematic literature review with two objectives: to understand the more relevant factors affecting energy consumption of buildings and...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/22/7810 |
| _version_ | 1797510413557956608 |
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| author | Ahmed Abdelaziz Vitor Santos Miguel Sales Dias |
| author_facet | Ahmed Abdelaziz Vitor Santos Miguel Sales Dias |
| author_sort | Ahmed Abdelaziz |
| collection | DOAJ |
| description | The high level of energy consumption of buildings is significantly influencing occupant behavior changes towards improved energy efficiency. This paper introduces a systematic literature review with two objectives: to understand the more relevant factors affecting energy consumption of buildings and to find the best intelligent computing (IC) methods capable of classifying and predicting energy consumption of different types of buildings. Adopting the PRISMA method, the paper analyzed 822 manuscripts from 2013 to 2020 and focused on 106, based on title and abstract screening and on manuscripts with experiments. A text mining process and a bibliometric map tool (VOS viewer) were adopted to find the most used terms and their relationships, in the energy and IC domains. Our approach shows that the terms “consumption,” “residential,” and “electricity” are the more relevant terms in the energy domain, in terms of the ratio of important terms (TITs), whereas “cluster” is the more commonly used term in the IC domain. The paper also shows that there are strong relations between “Residential Energy Consumption” and “Electricity Consumption,” “Heating” and “Climate. Finally, we checked and analyzed 41 manuscripts in detail, summarized their major contributions, and identified several research gaps that provide hints for further research. |
| first_indexed | 2024-03-10T05:31:09Z |
| format | Article |
| id | doaj.art-0bbd7863e7fa4433a8f9eecf94c2614f |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T05:31:09Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-0bbd7863e7fa4433a8f9eecf94c2614f2023-11-22T23:13:43ZengMDPI AGEnergies1996-10732021-11-011422781010.3390/en14227810Machine Learning Techniques in the Energy Consumption of Buildings: A Systematic Literature Review Using Text Mining and Bibliometric AnalysisAhmed Abdelaziz0Vitor Santos1Miguel Sales Dias2Nova Information Management School, Universidade Nova de Lisboa, 1070-312 Lisboa, PortugalNova Information Management School, Universidade Nova de Lisboa, 1070-312 Lisboa, PortugalDepartment of Information Science and Technology, Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, 1649-026 Lisboa, PortugalThe high level of energy consumption of buildings is significantly influencing occupant behavior changes towards improved energy efficiency. This paper introduces a systematic literature review with two objectives: to understand the more relevant factors affecting energy consumption of buildings and to find the best intelligent computing (IC) methods capable of classifying and predicting energy consumption of different types of buildings. Adopting the PRISMA method, the paper analyzed 822 manuscripts from 2013 to 2020 and focused on 106, based on title and abstract screening and on manuscripts with experiments. A text mining process and a bibliometric map tool (VOS viewer) were adopted to find the most used terms and their relationships, in the energy and IC domains. Our approach shows that the terms “consumption,” “residential,” and “electricity” are the more relevant terms in the energy domain, in terms of the ratio of important terms (TITs), whereas “cluster” is the more commonly used term in the IC domain. The paper also shows that there are strong relations between “Residential Energy Consumption” and “Electricity Consumption,” “Heating” and “Climate. Finally, we checked and analyzed 41 manuscripts in detail, summarized their major contributions, and identified several research gaps that provide hints for further research.https://www.mdpi.com/1996-1073/14/22/7810intelligent modelsenergy consumption of buildingssystematic literature reviewtext miningbibliometric mapmachine learning |
| spellingShingle | Ahmed Abdelaziz Vitor Santos Miguel Sales Dias Machine Learning Techniques in the Energy Consumption of Buildings: A Systematic Literature Review Using Text Mining and Bibliometric Analysis Energies intelligent models energy consumption of buildings systematic literature review text mining bibliometric map machine learning |
| title | Machine Learning Techniques in the Energy Consumption of Buildings: A Systematic Literature Review Using Text Mining and Bibliometric Analysis |
| title_full | Machine Learning Techniques in the Energy Consumption of Buildings: A Systematic Literature Review Using Text Mining and Bibliometric Analysis |
| title_fullStr | Machine Learning Techniques in the Energy Consumption of Buildings: A Systematic Literature Review Using Text Mining and Bibliometric Analysis |
| title_full_unstemmed | Machine Learning Techniques in the Energy Consumption of Buildings: A Systematic Literature Review Using Text Mining and Bibliometric Analysis |
| title_short | Machine Learning Techniques in the Energy Consumption of Buildings: A Systematic Literature Review Using Text Mining and Bibliometric Analysis |
| title_sort | machine learning techniques in the energy consumption of buildings a systematic literature review using text mining and bibliometric analysis |
| topic | intelligent models energy consumption of buildings systematic literature review text mining bibliometric map machine learning |
| url | https://www.mdpi.com/1996-1073/14/22/7810 |
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