ArticleHeliyon2024
A model of indoor thermal condition based on traditional acehnese houses using artificial neural network.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
No grant is acknowledged in the PubMed record.
Abstract
This paper examines the thermal condition aspects of traditional Acehnese houses (Rumoh Aceh) in Indonesia. This study highlights the importance of considering traditional architecture in achieving comfortable living conditions. Through the integration of cultural values, architectural design, and environmental factors, this research evaluates the prediction of thermal conditions within Rumoh Aceh based on building orientation and location, serving as guidelines for architects in designing buildings in the Aceh region. The study employs an ANN (Artificial Neural Network) algorithm to predict thermal condition parameters including indoor: temperature, humidity, and wind speed. The sample data used to train the ANN model consists of thermal data from five different rooms and meteorological data. The ANN model is employed to predict the thermal conditions in the Rumoh Aceh building based on its orientation and location. In this study, two models were developed, namely MODEL_01, for predicting indoor temperature and humidity, and MODEL_02, for predicting indoor wind speed. In MODEL_01, the error tolerance level obtained is ±1.05, and in MODEL_02, it is ±0.03. The prediction results based on the building orientation show the lowest average indoor temperature value of 29.19 °C, occurring at an angle of 292.19°. Furthermore, the lowest average indoor humidity value is 74.57 %, which also occurs at an angle of 292.19°. However, for indoor wind speed, the angle of 260° records the lowest average value of 0.125 m/s, while the highest average value is observed at 292.19°, at 0.132 m/s. The prediction results based on the building's location show the lowest average temperature value, which is 29.44 °C in the Aceh Besar region. Furthermore, the lowest average humidity value is 75.11 % in the North Aceh region. Meanwhile, the highest average wind speed value is 0.174 m/s in the Sabang region. This study reveals that at the orientation angle of 292.19° (the qibla direction), it produces optimal thermal conditions in terms of temperature and humidity with low wind speed. Other findings indicate that the Aceh Besar, North Aceh, and Sabang regions have optimal thermal conditions based on temperature, humidity, and wind speed.
Indexed as
Identifiers
What OpenQuestion holds
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.