Application of artificial intelligence in green building concept for energy auditing using drone technology under different environmental conditions

• Published in: Scientific reports Vol. 13; no. 1; pp. 8200 - 18
• Main Authors: Khan, Osama, Parvez, Mohd, Alansari, Monairah, Farid, Mohammad, Devarajan, Yuvarajan, Thanappan, Subash
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 21.05.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 639/4077; Artificial intelligence; Civil engineering; Climatic conditions; Developing countries; Drones; Efficiency; Energy; Environmental conditions; Green buildings; Green development; Heat; Humanities and Social Sciences; Humidity; Internet of Things; LDCs; Mechanical engineering; multidisciplinary; Relative humidity; Science; Science (multidisciplinary); Software; Temperature; Variables; Wind speed
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-35245-x

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.