Energy consumption forecasting in PCM-integration buildings considering building and environmental parameters for future climate scenarios

• Published in: Energy (Oxford) 2024-11, Vol.310, p.133248, Article 133248
• Main Authors: Aliyeva, Xeniya, Memon, Shazim Ali, Nazir, Kashif, Kim, Jong
• Format: Article
• Language: English
• Subjects: Decision tree; Energy consumption; Machine learning; Parametric analysis; Phase change material
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2024.133248

Despite numerous machine learning methods being employed to predict the energy consumption of PCM-integrated buildings, key research gaps remain. Most studies focus solely on building parameters while omitting important environmental parameters, including precipitation and air pressure. No study evaluated and proposed prediction models for PCM-integrated buildings considering future climate scenarios. Also, as per the authors′ knowledge, no researcher has assessed the impact of variations in the hyperparameter, especially for decision tree-based prediction models to develop a reliable prediction model with less complexity and a high degree of interpretability between independent and dependent variables. This research addresses these gaps by evaluating fine, medium, and coarse decision trees for predicting energy consumption in PCM-integrated buildings under future climate scenarios by considering extensive building and environmental parameters simultaneously. A database for energy consumption was created through energy simulations for 11 cities in hot semi-arid climates. The Fine Decision Tree (FDT3) emerged as the most accurate prediction model, with R2 values over 94 % in training and testing phases based on model evaluation and validation processes. Parametric analysis further revealed that both environmental and building parameters are crucial in accurately predicting the energy consumption of PCM-integrated buildings using FDT3. •Proposed decision tree based prediction model for PCM-integrated building.•Considered building and environmental parameters to predict EC in BSh zone.•Evaluated role of hyperparameters on the accuracy and complexity of the model.•Assessed the model's performance through external validation on unforeseen data.•FDT3 was the most reliable and accurate prediction model (R2 > 94 %).