An artificial neural network-based system to estimate the thermal comfort of buildings with energy efficiency
Heating Ventilation Air Condition (HVAC) systems consume the majority of energy in a building; it is essential to optimize this energy while improving the thermal comfort of the occupants. The Predict Mean Vote (PMV) model is considered as one of the most efficient models to define the thermal comfo...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2023-01-01
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| Series: | ITM Web of Conferences |
| Subjects: | |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2023/02/itmconf_cocia2023_02003.pdf |
| _version_ | 1797829374537367552 |
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| author | Boutahri Youssef Tilioua Amine |
| author_facet | Boutahri Youssef Tilioua Amine |
| author_sort | Boutahri Youssef |
| collection | DOAJ |
| description | Heating Ventilation Air Condition (HVAC) systems consume the majority of energy in a building; it is essential to optimize this energy while improving the thermal comfort of the occupants. The Predict Mean Vote (PMV) model is considered as one of the most efficient models to define the thermal comfort of a structure. In this context this paper proposes a prediction of PMV index using ANN algorithm to classify the real-time thermal comfort states of occupants, which may provide future energy savings by adopting time-varying setpoints where real-time changes in thermal comfort may require less energy. The performance of studied algorithm was tested using several evaluation parameters such as mean square error (MSE) and correlation coefficient (R2). The algorithm studied in this article showed promising results in terms of correlation coefficient R2 and MSE. |
| first_indexed | 2024-04-09T13:20:30Z |
| format | Article |
| id | doaj.art-ec23e6bee6674c0cba49668fe3766cfc |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-04-09T13:20:30Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-ec23e6bee6674c0cba49668fe3766cfc2023-05-11T09:11:01ZengEDP SciencesITM Web of Conferences2271-20972023-01-01520200310.1051/itmconf/20235202003itmconf_cocia2023_02003An artificial neural network-based system to estimate the thermal comfort of buildings with energy efficiencyBoutahri Youssef0Tilioua Amine1Research Team in Thermal and Applied Thermodynamics (2.T.A.), Mechanics, Energy Efficiency and Renewable Energies Laboratory (L.M.3.E.R.), Faculty of Sciences and Techniques Errachidia, Moulay Ismaïl UniversityResearch Team in Thermal and Applied Thermodynamics (2.T.A.), Mechanics, Energy Efficiency and Renewable Energies Laboratory (L.M.3.E.R.), Faculty of Sciences and Techniques Errachidia, Moulay Ismaïl UniversityHeating Ventilation Air Condition (HVAC) systems consume the majority of energy in a building; it is essential to optimize this energy while improving the thermal comfort of the occupants. The Predict Mean Vote (PMV) model is considered as one of the most efficient models to define the thermal comfort of a structure. In this context this paper proposes a prediction of PMV index using ANN algorithm to classify the real-time thermal comfort states of occupants, which may provide future energy savings by adopting time-varying setpoints where real-time changes in thermal comfort may require less energy. The performance of studied algorithm was tested using several evaluation parameters such as mean square error (MSE) and correlation coefficient (R2). The algorithm studied in this article showed promising results in terms of correlation coefficient R2 and MSE.https://www.itm-conferences.org/articles/itmconf/pdf/2023/02/itmconf_cocia2023_02003.pdfenergyhvac systemsthermal comfortmachine learningartificial neural network |
| spellingShingle | Boutahri Youssef Tilioua Amine An artificial neural network-based system to estimate the thermal comfort of buildings with energy efficiency ITM Web of Conferences energy hvac systems thermal comfort machine learning artificial neural network |
| title | An artificial neural network-based system to estimate the thermal comfort of buildings with energy efficiency |
| title_full | An artificial neural network-based system to estimate the thermal comfort of buildings with energy efficiency |
| title_fullStr | An artificial neural network-based system to estimate the thermal comfort of buildings with energy efficiency |
| title_full_unstemmed | An artificial neural network-based system to estimate the thermal comfort of buildings with energy efficiency |
| title_short | An artificial neural network-based system to estimate the thermal comfort of buildings with energy efficiency |
| title_sort | artificial neural network based system to estimate the thermal comfort of buildings with energy efficiency |
| topic | energy hvac systems thermal comfort machine learning artificial neural network |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2023/02/itmconf_cocia2023_02003.pdf |
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