Titre :

Machine Learning-Based Prediction of Cooling and Heating Energy Consumption for PCM Integrated a Residential Building Envelope

Auteur(s) :

Salihi, Mustapha [Auteur]
Université Mohammed VI Polytechnique = Mohammed VI Polytechnic University [Ben Guerir] [UM6P]
Université Ibn Tofaïl [UIT]
El Fiti, Maryam [Auteur]
Université Mohammed VI Polytechnique = Mohammed VI Polytechnic University [Ben Guerir] [UM6P]
Université Ibn Tofaïl [UIT]
Harmen, Yasser [Auteur]
Université Mohammed VI Polytechnique = Mohammed VI Polytechnic University [Ben Guerir] [UM6P]
Université Ibn Tofaïl [UIT]
Chhiti, Younes [Auteur]
Université Mohammed VI Polytechnique = Mohammed VI Polytechnic University [Ben Guerir] [UM6P]
Université Ibn Tofaïl [UIT]
Chebak, Ahmed [Auteur]
Université Mohammed VI Polytechnique = Mohammed VI Polytechnic University [Ben Guerir] [UM6P]
Jama, Charafeddine [Auteur]
Unité Matériaux et Transformations (UMET) - UMR 8207

Titre de la manifestation scientifique :

2024 8th International Conference on Green Energy and Applications (ICGEA)

Ville :

Singapore

Pays :

Singapour

Date de début de la manifestation scientifique :

2024-03-14

Éditeur :

IEEE

Date de publication :

2024-03-14

Mot(s)-clé(s) en anglais :

Energy consumption
Cooling
Computational modeling
Buildings
Artificial neural networks
Machine learning
energy consumption
Phase Change Material (PCM)
residential building

Discipline(s) HAL :

Chimie/Matériaux

Résumé en anglais : [en]

Latent heat thermal energy storage technologies using Phase Change Materials (PCMs) are a promising solution to enhance building thermal performance and reduce energy consumption. However, conducting experimental or numerical ...
Lire la suite >Latent heat thermal energy storage technologies using Phase Change Materials (PCMs) are a promising solution to enhance building thermal performance and reduce energy consumption. However, conducting experimental or numerical studies becomes time-consuming and computationally expensive due to the non-linearity and complexity associated with climatic conditions and variations in the thermophysical properties of PCMs within the building envelope. Thus, effective energy demand forecasting is essential for optimizing planning and minimizing energy consumption in buildings. Machine learning techniques have become increasingly popular due to their reliability and cost-effectiveness. This study uses machine learning models to predict the energy consumption of PCM-integrated residential building envelopes. Five well-known machine learning models were investigated: Multiple Linear Regression (MLR), Support Vector Regression (SVR), Artificial Neural Network (ANN), Generalized Additive Model (GAM), and Decision Tree (DT). These models considered PCM thermophysical properties, location, and thickness variations. The dataset was generated through a parametric analysis using EnergyPlus and JEplus simulation tools. The prediction computations were conducted using a computer program written in Python-based software. Thereafter, model performance was assessed using three metrics: R2, MAE, and RMSE. The results indicate that the ANN model outperformed others, achieving the lowest RMSE, MAE, and the highest R-squared value exceeding 0.99. Moreover, this study's findings emphasize the potential of the ANN model in predicting energy consumption and offer valuable insights for stakeholders aiming to optimize heating and cooling energy consumption in PCM-incorporated residential buildings.Lire moins >

Langue :

Anglais

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

Université de Lille
CNRS
INRAE
ENSCL

Collections :

• Unité Matériaux et Transformations (UMET) - UMR 8207

Équipe(s) de recherche :

Procédés de Recyclage et de Fonctionnalisation (PReF)

Date de dépôt :

2024-09-03T14:17:35Z
2024-09-04T09:19:34Z