ArticleScientific reports2023
Application of artificial intelligence in green building concept for energy auditing using drone technology under different environmental conditions.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Entropy-Driven Environmental Impact Assessment of Condensate-Induced Irreversibility in Integrated Building Energy Systems.Entropy (Basel, Switzerland) · 2026Article
- AI-Integrated autonomous robotics for solar panel cleaning and predictive maintenance using drone and ground-based systems.Scientific reports · 2025Article
- Article
- Research on Lateral Safety Spacing for Fusion Operation Based on Unmanned and Manned Aircraft-Event Modeling.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
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
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.