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Application of artificial intelligence in green building concept for energy auditing using drone technology under different environmental conditions
Thermal losses through weak building envelope is responsible for global current energy crises. Application of artificial intelligence and drone setups in green buildings can help in providing the sustainable solution the world is striving for years. The contemporary research incorporates a novel con...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10200798/ https://www.ncbi.nlm.nih.gov/pubmed/37211551 http://dx.doi.org/10.1038/s41598-023-35245-x |
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author | Khan, Osama Parvez, Mohd Alansari, Monairah Farid, Mohammad Devarajan, Yuvarajan Thanappan, Subash |
author_facet | Khan, Osama Parvez, Mohd Alansari, Monairah Farid, Mohammad Devarajan, Yuvarajan Thanappan, Subash |
author_sort | Khan, Osama |
collection | PubMed |
description | Thermal losses through weak building envelope is responsible for global current energy crises. Application of artificial intelligence and drone setups in green buildings can help in providing the sustainable solution the world is striving for years. The contemporary research incorporates a novel concept of measuring the wearing thermal resistances in the building envelope with the aid of a drone system. The above procedure conducts a throughout building analysis by considering three prime environmental parameters such as wind speed (WS), relative humidity (RH) and dry bulb temperature (DBT) with the aid of drone heat mapping procedure. The novelty of the study can be interpreted by the fact that prior researches have never explored the building envelope through a combination of drone and climatic conditions as variables in building areas difficult to access, thereby providing an easier, risk free, cost effective and efficient reading. Validation of the formula is authenticated by employing artificial intelligence-based software’s which are applied for data prediction and optimization. Artificial models are established to validate the variables for each output from the specified number of climatic inputs. The pareto-optimal conditions attained after analysis are 44.90% RH, 12.61 °C DBT and 5.20 km/h WS. The variables and thermal resistance were validated with response surface methodology method, thereby presenting lowest error rate and comprehensive R(2) value, which are 0.547 and 0.97, respectively. Henceforth, employing drone-based technology in estimating building envelope discrepancies with the novel formula, yields consistent and effective assessment for development of green building, simultaneously reducing time and cost of the experimentation. |
format | Online Article Text |
id | pubmed-10200798 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-102007982023-05-23 Application of artificial intelligence in green building concept for energy auditing using drone technology under different environmental conditions Khan, Osama Parvez, Mohd Alansari, Monairah Farid, Mohammad Devarajan, Yuvarajan Thanappan, Subash Sci Rep Article Thermal losses through weak building envelope is responsible for global current energy crises. Application of artificial intelligence and drone setups in green buildings can help in providing the sustainable solution the world is striving for years. The contemporary research incorporates a novel concept of measuring the wearing thermal resistances in the building envelope with the aid of a drone system. The above procedure conducts a throughout building analysis by considering three prime environmental parameters such as wind speed (WS), relative humidity (RH) and dry bulb temperature (DBT) with the aid of drone heat mapping procedure. The novelty of the study can be interpreted by the fact that prior researches have never explored the building envelope through a combination of drone and climatic conditions as variables in building areas difficult to access, thereby providing an easier, risk free, cost effective and efficient reading. Validation of the formula is authenticated by employing artificial intelligence-based software’s which are applied for data prediction and optimization. Artificial models are established to validate the variables for each output from the specified number of climatic inputs. The pareto-optimal conditions attained after analysis are 44.90% RH, 12.61 °C DBT and 5.20 km/h WS. The variables and thermal resistance were validated with response surface methodology method, thereby presenting lowest error rate and comprehensive R(2) value, which are 0.547 and 0.97, respectively. Henceforth, employing drone-based technology in estimating building envelope discrepancies with the novel formula, yields consistent and effective assessment for development of green building, simultaneously reducing time and cost of the experimentation. Nature Publishing Group UK 2023-05-21 /pmc/articles/PMC10200798/ /pubmed/37211551 http://dx.doi.org/10.1038/s41598-023-35245-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Khan, Osama Parvez, Mohd Alansari, Monairah Farid, Mohammad Devarajan, Yuvarajan Thanappan, Subash Application of artificial intelligence in green building concept for energy auditing using drone technology under different environmental conditions |
title | Application of artificial intelligence in green building concept for energy auditing using drone technology under different environmental conditions |
title_full | Application of artificial intelligence in green building concept for energy auditing using drone technology under different environmental conditions |
title_fullStr | Application of artificial intelligence in green building concept for energy auditing using drone technology under different environmental conditions |
title_full_unstemmed | Application of artificial intelligence in green building concept for energy auditing using drone technology under different environmental conditions |
title_short | Application of artificial intelligence in green building concept for energy auditing using drone technology under different environmental conditions |
title_sort | application of artificial intelligence in green building concept for energy auditing using drone technology under different environmental conditions |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10200798/ https://www.ncbi.nlm.nih.gov/pubmed/37211551 http://dx.doi.org/10.1038/s41598-023-35245-x |
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