Adapting Machine Learning Models for Indoor Temperature Prediction from Kuwait to the French Riviera Climate
This study investigates how machine learning models that were first created to forecast indoor temperatures in Kuwaiti portable cabins may be modified to replicate indoor temperature conditions in Nice, France. Two thermally identical portable cabins were built in Kuwait. Indoor weather conditions a...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/36/e3sconf_icsree2025_04001.pdf |
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| author | Sedaghat Ahmad Nazififard Mohammad Franquet Erwin Kalbasi Rasool Mostafaeipour Ali |
| author_facet | Sedaghat Ahmad Nazififard Mohammad Franquet Erwin Kalbasi Rasool Mostafaeipour Ali |
| author_sort | Sedaghat Ahmad |
| collection | DOAJ |
| description | This study investigates how machine learning models that were first created to forecast indoor temperatures in Kuwaiti portable cabins may be modified to replicate indoor temperature conditions in Nice, France. Two thermally identical portable cabins were built in Kuwait. Indoor weather conditions and energy consumption were measured, and the data was stored using Internet of Things (IoT). A transient system simulation (TRNSYS) model and several machine learning (ML) models were developed and validated against experimental data over the full years of 2023 and 2024. A total of nineteen regression (ML) models are examined. The Matern 5/2 Gaussian process regression model has done somewhat better at modeling interior temperature; all models are shown to function well. In this work, the predicted indoor temperatures are presented and discussed for Kuwait and Nice, highlighting the similarity of temperature profiles across these two distinct climate regions. The research aims to assess the effectiveness and accuracy of these models across different climatic conditions, contributing to the development of energy-efficient solutions for smart buildings. |
| format | Article |
| id | doaj-art-0ad24ce8d05243c79400b4d882603584 |
| institution | Kabale University |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-0ad24ce8d05243c79400b4d8826035842025-08-20T03:29:40ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016360400110.1051/e3sconf/202563604001e3sconf_icsree2025_04001Adapting Machine Learning Models for Indoor Temperature Prediction from Kuwait to the French Riviera ClimateSedaghat Ahmad0Nazififard Mohammad1Franquet Erwin2Kalbasi Rasool3Mostafaeipour Ali4Department of Mechanical Engineering, College of Engineering, Australian UniversityUniversité Côte d’Azur, Polytech’LabUniversité Côte d’Azur, Polytech’LabDepartment of Mechanical Engineering, Najafabad Branch, Islamic Azad UniversityCivil and Environmental Engineering Department, California State UniversityThis study investigates how machine learning models that were first created to forecast indoor temperatures in Kuwaiti portable cabins may be modified to replicate indoor temperature conditions in Nice, France. Two thermally identical portable cabins were built in Kuwait. Indoor weather conditions and energy consumption were measured, and the data was stored using Internet of Things (IoT). A transient system simulation (TRNSYS) model and several machine learning (ML) models were developed and validated against experimental data over the full years of 2023 and 2024. A total of nineteen regression (ML) models are examined. The Matern 5/2 Gaussian process regression model has done somewhat better at modeling interior temperature; all models are shown to function well. In this work, the predicted indoor temperatures are presented and discussed for Kuwait and Nice, highlighting the similarity of temperature profiles across these two distinct climate regions. The research aims to assess the effectiveness and accuracy of these models across different climatic conditions, contributing to the development of energy-efficient solutions for smart buildings.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/36/e3sconf_icsree2025_04001.pdfenergyinternet of thingsmachine learningportable cabinssmart building |
| spellingShingle | Sedaghat Ahmad Nazififard Mohammad Franquet Erwin Kalbasi Rasool Mostafaeipour Ali Adapting Machine Learning Models for Indoor Temperature Prediction from Kuwait to the French Riviera Climate E3S Web of Conferences energy internet of things machine learning portable cabins smart building |
| title | Adapting Machine Learning Models for Indoor Temperature Prediction from Kuwait to the French Riviera Climate |
| title_full | Adapting Machine Learning Models for Indoor Temperature Prediction from Kuwait to the French Riviera Climate |
| title_fullStr | Adapting Machine Learning Models for Indoor Temperature Prediction from Kuwait to the French Riviera Climate |
| title_full_unstemmed | Adapting Machine Learning Models for Indoor Temperature Prediction from Kuwait to the French Riviera Climate |
| title_short | Adapting Machine Learning Models for Indoor Temperature Prediction from Kuwait to the French Riviera Climate |
| title_sort | adapting machine learning models for indoor temperature prediction from kuwait to the french riviera climate |
| topic | energy internet of things machine learning portable cabins smart building |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/36/e3sconf_icsree2025_04001.pdf |
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